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Mastering 1:1s as a Data Scientist: From Status Updates to Career Growth

I have been a data team manager for six months, and my team has grown from three to five. I wrote about my initial manager experiences back in November. In this article, I want to talk about something that is more essential to the relationship between a DS or DA individual contributor (IC) and their manager — the 1:1 meetings. I remember when I first started my career, I felt nervous and awkward in my 1:1s, as I didn’t know what to expect or what was useful. Now, having been on both sides during 1:1s, I understand better how to have an effective 1:1 meeting. If you have ever struggled with how to make the best out of your 1:1s, here are my essential tips. I. Set up a regular 1:1 cadence First and foremost, 1:1 meetings with your manager should happen regularly. It could be weekly or biweekly, depending on the pace of your projects. For example, if you are more analytics-focused and have lots of fast-moving reporting and analysis tasks, a weekly 1:1 might be better to provide timely updates and align on project prioritization. However, if you are focusing on a long-term machine learning project that will span multiple weeks, you might feel more comfortable with a biweekly cadence — this allows you to do your research, try different approaches, and have meaningful conversations during 1:1s. I have weekly recurring 30-minute 1:1 slots with everyone on my team, just to make sure I always have this dedicated time for them every week. These meetings sometimes end up being short 15-minute chats or even casual conversations about life after work, but I still find them super helpful for staying updated on what’s on top of everyone’s mind and building personal connections. II. Make preparations and update your 1:1 agenda Preparing for your 1:1 is critical. I maintain a shared 1:1 document with my manager and update it every week before our meetings. I also appreciate my direct reports preparing their 1:1 agenda beforehand. Here is why: Throughout the week, I like to jot down discussion topics quickly on my 1:1 doc whenever they come to my mind. This ensures I cover all important points during the meeting and improves communication effectiveness. Having an agenda helps both you and your manager keep track of what has been discussed and keeps everyone accountable. We talk to many people every day, so it is totally normal if you lose track of what you have mentioned to someone. Therefore, having such a doc reminds you of your previous conversations. Now, as a manager with a team of five, I also turn to the 1:1 docs to ensure I address all open questions and action items from the last meeting and find links to past projects. It can also assist your performance review process. When writing my self-review, I read through my 1:1 doc to list my achievements. Similarly, I also use the 1:1 docs with my team to make sure I do not miss any highlights from their projects. So, what are good topics for 1:1? See the section below. III. Topics on your 1:1 agenda While each manager has their preferences, there’s a wide range of topics that are generally appropriate for 1:1s. You don’t have to cover every one of them, but I hope they give you some inspiration and you no longer feel clueless about your 1:1. Achievements since the last 1:1: I recommend listing the latest achievements in your 1:1 doc. You don’t have to talk about each one in detail during the meeting, but it’s good to give your manager visibility and remind them how good you are 🙂. It is also a good idea to highlight both your effort and impact. Business is usually impact-driven, and the data team is no exception. If your A/B test leads to a go/no-go decision, mention that in the meeting. If your analysis leads to a product idea, bring it up and discuss how you plan to support the development and measure the impact. Ongoing and upcoming projects: One common pattern I’ve observed in my 7-year career is that Data Teams usually have long backlogs with numerous “urgent” requests. 1:1 is a good time to align with your manager on shifting priorities and timelines. If your project is blocked, let your manager know. While independence is always appreciated, unexpected blockers can arise at anytime. It’s perfectly acceptable to work through the blockers with your manager, as they typically have more experience and are supposed to empower you to complete your projects. It is better to let your manager know ahead of time instead of letting them find out themselves later and ask you why you missed the timeline. Meanwhile, ideally, you don’t just bring up the blockers but also suggest possible solutions or ask for specific help. For example, “I am blocked on accessing X data. Should I prioritize building the data pipeline with the data engineer or push for an ad-hoc pull?” This shows you are a true problem-solver with a growth mindset. Career growth: You can also use the 1:1 time to talk about career growth topics. Career growth for data scientists isn’t just about promotions. You might be more interested in growing technical expertise in a specific domain, such as experimentation, or moving from DS to different functions like MLE, or gaining Leadership experience and transitioning to a people management role, just like me. To make sure you are moving towards your career goal, you should have this conversation with your manager regularly so they can provide corresponding advice and match you with projects that align with your long-term goal. I also have monthly career growth check-in sessions with my team to specifically talk about career progress. If you always find your 1:1 time being occupied by project updates, consider setting up a separate meeting like this with your manager. Feedback: Feedback should go both directions. Your manager likely does not have as much time to work on data projects as you do. Therefore, you might notice inefficiencies in project workflows, analysis processes, or cross-functional collaboration that they aren’t aware of. Don’t hesitate to bring these up. And similar to handling blockers, it’s recommended to think about potential solutions before going to the meeting to show your manager you are a team player who contributes to the team’s culture and success. For example, instead of saying, “We’re getting too many ad-hoc requests,” frame it as “Ad-hoc requests coming through Slack DMs reduce our focus time on planned projects. Could we invite stakeholders to our sprint planning meetings to align on priorities and have a more formal request intake process during the sprint?” Meanwhile, you can also use this opportunity to ask your manager for any feedback on your performance. This helps you identify gaps, improve continuously, and ensures there are no surprises during your official performance review 🙂 Team and company goals: Change is the only constant in business. Data teams work closely with stakeholders, so data scientists need to understand the company’s priorities and what matters most at the moment. For example, if your company is focusing on retention, you might want to analyze drivers of higher retention and propose corresponding marketing campaign ideas to your stakeholder. To give you a more concrete idea of the 1:1 agenda, let’s assume you work at a consumer bank and focus on the credit card rewards domain. Here is a sample agenda: Date: 03/03/2025 ✅ Last week’s accomplishments Rewards A/B test analysis [link]: Shared with stakeholders, and we will launch the winning treatment A to broader users in Q1. Rewards redemption analysis [link]: Most users redeem rewards for statement balance. Talking to the marketing team to run an email campaign advertising other redemption options. 🗒 Ongoing projects [P0] Rewards churn analysis: Understand if rewards activities are correlated with churn. ETA 3/7. [P1] Rewards costs dashboard: Build a dashboard tracking the costs of all rewards activities. ETA 3/12. [Blocked] Travel credit usage dashboard: Waiting for DE to set up the travel booking table. Followed up on 2/27. Need escalation? [Deprioritized] Retail merchant bonus rewards campaign support: This was deprioritized by the marketing team as we delayed the campaign. 🔍 Other topics I would like to gain more experience in machine learning. Are there any project opportunities? Any feedback on my collaboration with the stakeholder? Please also keep in mind that you should update your 1:1 doc actively during the meeting. It should reflect what is discussed and include important notes for each bullet point. You can even add an ‘Action Items’ section at the bottom of each meeting agenda to make the next steps clear. Final thoughts Above are my essential tips to run effective 1:1s as a data scientist. By establishing regular meetings, preparing thoughtful agendas, and covering meaningful topics, you can transform these meetings from awkward status updates into valuable growth opportunities. Remember, your 1:1 isn’t just about updating your manager — it’s about getting the support, guidance, and visibility you need to grow in your role.

I have been a data team manager for six months, and my team has grown from three to five.

I wrote about my initial manager experiences back in November. In this article, I want to talk about something that is more essential to the relationship between a DS or DA individual contributor (IC) and their manager — the 1:1 meetings. I remember when I first started my career, I felt nervous and awkward in my 1:1s, as I didn’t know what to expect or what was useful. Now, having been on both sides during 1:1s, I understand better how to have an effective 1:1 meeting.

If you have ever struggled with how to make the best out of your 1:1s, here are my essential tips.

I. Set up a regular 1:1 cadence

First and foremost, 1:1 meetings with your manager should happen regularly. It could be weekly or biweekly, depending on the pace of your projects. For example, if you are more analytics-focused and have lots of fast-moving reporting and analysis tasks, a weekly 1:1 might be better to provide timely updates and align on project prioritization. However, if you are focusing on a long-term machine learning project that will span multiple weeks, you might feel more comfortable with a biweekly cadence — this allows you to do your research, try different approaches, and have meaningful conversations during 1:1s.

I have weekly recurring 30-minute 1:1 slots with everyone on my team, just to make sure I always have this dedicated time for them every week. These meetings sometimes end up being short 15-minute chats or even casual conversations about life after work, but I still find them super helpful for staying updated on what’s on top of everyone’s mind and building personal connections.

II. Make preparations and update your 1:1 agenda

Preparing for your 1:1 is critical. I maintain a shared 1:1 document with my manager and update it every week before our meetings. I also appreciate my direct reports preparing their 1:1 agenda beforehand. Here is why:

  • Throughout the week, I like to jot down discussion topics quickly on my 1:1 doc whenever they come to my mind. This ensures I cover all important points during the meeting and improves communication effectiveness.
  • Having an agenda helps both you and your manager keep track of what has been discussed and keeps everyone accountable. We talk to many people every day, so it is totally normal if you lose track of what you have mentioned to someone. Therefore, having such a doc reminds you of your previous conversations. Now, as a manager with a team of five, I also turn to the 1:1 docs to ensure I address all open questions and action items from the last meeting and find links to past projects.
  • It can also assist your performance review process. When writing my self-review, I read through my 1:1 doc to list my achievements. Similarly, I also use the 1:1 docs with my team to make sure I do not miss any highlights from their projects.

So, what are good topics for 1:1? See the section below.

III. Topics on your 1:1 agenda

While each manager has their preferences, there’s a wide range of topics that are generally appropriate for 1:1s. You don’t have to cover every one of them, but I hope they give you some inspiration and you no longer feel clueless about your 1:1.

  • Achievements since the last 1:1: I recommend listing the latest achievements in your 1:1 doc. You don’t have to talk about each one in detail during the meeting, but it’s good to give your manager visibility and remind them how good you are 🙂. It is also a good idea to highlight both your effort and impact. Business is usually impact-driven, and the data team is no exception. If your A/B test leads to a go/no-go decision, mention that in the meeting. If your analysis leads to a product idea, bring it up and discuss how you plan to support the development and measure the impact.
  • Ongoing and upcoming projects: One common pattern I’ve observed in my 7-year career is that Data Teams usually have long backlogs with numerous “urgent” requests. 1:1 is a good time to align with your manager on shifting priorities and timelines.
    • If your project is blocked, let your manager know. While independence is always appreciated, unexpected blockers can arise at anytime. It’s perfectly acceptable to work through the blockers with your manager, as they typically have more experience and are supposed to empower you to complete your projects. It is better to let your manager know ahead of time instead of letting them find out themselves later and ask you why you missed the timeline. Meanwhile, ideally, you don’t just bring up the blockers but also suggest possible solutions or ask for specific help. For example, “I am blocked on accessing X data. Should I prioritize building the data pipeline with the data engineer or push for an ad-hoc pull?” This shows you are a true problem-solver with a growth mindset.
  • Career growth: You can also use the 1:1 time to talk about career growth topics. Career growth for data scientists isn’t just about promotions. You might be more interested in growing technical expertise in a specific domain, such as experimentation, or moving from DS to different functions like MLE, or gaining Leadership experience and transitioning to a people management role, just like me. To make sure you are moving towards your career goal, you should have this conversation with your manager regularly so they can provide corresponding advice and match you with projects that align with your long-term goal.
    • I also have monthly career growth check-in sessions with my team to specifically talk about career progress. If you always find your 1:1 time being occupied by project updates, consider setting up a separate meeting like this with your manager.
  • Feedback: Feedback should go both directions.
    • Your manager likely does not have as much time to work on data projects as you do. Therefore, you might notice inefficiencies in project workflows, analysis processes, or cross-functional collaboration that they aren’t aware of. Don’t hesitate to bring these up. And similar to handling blockers, it’s recommended to think about potential solutions before going to the meeting to show your manager you are a team player who contributes to the team’s culture and success. For example, instead of saying, “We’re getting too many ad-hoc requests,” frame it as “Ad-hoc requests coming through Slack DMs reduce our focus time on planned projects. Could we invite stakeholders to our sprint planning meetings to align on priorities and have a more formal request intake process during the sprint?”
    • Meanwhile, you can also use this opportunity to ask your manager for any feedback on your performance. This helps you identify gaps, improve continuously, and ensures there are no surprises during your official performance review 🙂
  • Team and company goals: Change is the only constant in business. Data teams work closely with stakeholders, so data scientists need to understand the company’s priorities and what matters most at the moment. For example, if your company is focusing on retention, you might want to analyze drivers of higher retention and propose corresponding marketing campaign ideas to your stakeholder.

To give you a more concrete idea of the 1:1 agenda, let’s assume you work at a consumer bank and focus on the credit card rewards domain. Here is a sample agenda:

Date: 03/03/2025

✅ Last week’s accomplishments

  • Rewards A/B test analysis [link]: Shared with stakeholders, and we will launch the winning treatment A to broader users in Q1.
  • Rewards redemption analysis [link]: Most users redeem rewards for statement balance. Talking to the marketing team to run an email campaign advertising other redemption options.

🗒 Ongoing projects

  • [P0] Rewards churn analysis: Understand if rewards activities are correlated with churn. ETA 3/7.
  • [P1] Rewards costs dashboard: Build a dashboard tracking the costs of all rewards activities. ETA 3/12.
  • [Blocked] Travel credit usage dashboard: Waiting for DE to set up the travel booking table. Followed up on 2/27. Need escalation?
  • [Deprioritized] Retail merchant bonus rewards campaign support: This was deprioritized by the marketing team as we delayed the campaign.

🔍 Other topics

  • I would like to gain more experience in machine learning. Are there any project opportunities?
  • Any feedback on my collaboration with the stakeholder?

Please also keep in mind that you should update your 1:1 doc actively during the meeting. It should reflect what is discussed and include important notes for each bullet point. You can even add an ‘Action Items’ section at the bottom of each meeting agenda to make the next steps clear.

Final thoughts

Above are my essential tips to run effective 1:1s as a data scientist. By establishing regular meetings, preparing thoughtful agendas, and covering meaningful topics, you can transform these meetings from awkward status updates into valuable growth opportunities. Remember, your 1:1 isn’t just about updating your manager — it’s about getting the support, guidance, and visibility you need to grow in your role.

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No joke: data centers are warming the planet

The researchers also made use of a database provided by the International Energy Agency (IEA) that the authors pointed out contains more than 11,000 locations worldwide, of which 8,472 have been detected to dwell outside of highly dense urban areas. The latter locations were then used to “quantify the effect of data centers on the environment in terms of the LST gradient that could be measured on the areas surrounding each data center.” Asking the wrong question Asked if AI data centers are really causing local warming, or if this phenomenon is overstated, Sanchit Vir Gogia, chief analyst at Greyhound Research, said, “the signal is real, but the industry is asking the wrong question. The research shows a consistent rise in land surface temperature of around 2°C  following the establishment of large data centre facilities.” The debate, however, “has quickly shifted to causality: whether this is driven by operational heat from compute, or by land transformation during construction. That distinction matters scientifically, but it does not change the strategic implication.” Land surface temperature, said Gogia, is not the same as air temperature, and that gap will be used to challenge the findings. “But dismissing the signal on that basis would be a mistake,” he noted. “Data centers concentrate energy use, replace natural surfaces with heat-retaining materials, and continuously reject heat into the environment. Those are known drivers of thermal change.” He added, “the uncomfortable truth is this: Even if the exact mechanism is debated, the outcome aligns with first principles. Infrastructure at this scale alters its surroundings. The industry does not yet have a clean way to separate construction impact from operational impact, and that ambiguity makes the risk harder to model, not easier. This is not overstated, it is under-interpreted.” Location strategy must change But will the findings change

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Schneider Electric Maps the AI Data Center’s Next Design Era

The coming shift to higher-voltage DC That internal power challenge led Simonelli to one of the most consequential architectural topics in the interview: the likely transition toward higher-voltage DC distribution at very high rack densities. He framed it pragmatically. At current density levels, the industry knows how to get power into racks at 200 or 300 kilowatts. But as densities rise toward 400 kilowatts and beyond, conventional AC approaches start to run into physical limits. Too much cable, too much copper, too much conversion equipment, and too much space consumed by power infrastructure rather than GPUs. At that point, he said, higher-voltage DC becomes attractive not for philosophical reasons, but because it reduces current, shrinks conductor size, saves space, and leaves more room for revenue-generating compute. “It is again a paradigm shift,” Simonelli said of DC power at these densities. “But it won’t be everywhere.” That is probably right. The transition will not be universal, and the exact thresholds will evolve. But his underlying point is powerful. As rack densities climb, electrical architecture starts to matter not only for efficiency and reliability, but for physical space allocation inside the rack. Put differently, power distribution becomes a compute-enablement issue. Distance between accelerators matters, too. The closer GPUs and TPUs can be kept together, the better they perform. If power infrastructure can be compacted, more of the rack can be devoted to dense compute, improving the economics and performance of the system. That is a strong example of how AI is collapsing traditional boundaries between facility engineering and compute architecture. The two are no longer cleanly separable. Gas now, renewables over time On onsite power, Simonelli was refreshingly direct. If the goal is dispatchable onsite generation at the scale now being contemplated for AI facilities, he said, “there really isn’t an alternative

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SoftBank’s 10 GW Ohio Campus Marks a Turning Point for AI Infrastructure

Renewables can reduce carbon intensity, but they cannot independently meet the need for continuous, multi-gigawatt firm capacity without large-scale storage and balancing resources. For developers targeting guaranteed availability within this decade, natural gas remains the most readily deployable option, despite the political and environmental tradeoffs it introduces. AEP and the Cost Allocation Model If the generation plan explains the engineering logic, the AEP structure speaks to the political one. At the center is one of the most contested questions in the data center market: who pays for the transmission and grid upgrades required to serve large new loads? Utilities, regulators, consumer advocates, and large-load customers are increasingly divided on this issue. Data center developers point to economic development benefits, including jobs and tax revenue. Consumer advocates counter that residential ratepayers should not subsidize infrastructure built primarily to serve hyperscale demand. The Ohio arrangement is being positioned as a response to that conflict. DOE states that SB Energy and AEP Ohio are partnering on $4.2 billion in new transmission infrastructure, with SB Energy committing to fund those investments rather than passing costs through to ratepayers. AEP has echoed that position, indicating the structure is intended to avoid upward pressure on transmission rates for Ohio customers. Whether that outcome holds will depend on regulatory review and execution. But the structure itself is significant. It frames a model in which large-load developers directly fund the transmission infrastructure required to support their projects, rather than relying on broader cost recovery mechanisms. That makes the project more than a construction milestone. It positions it as a potential policy template. If validated, this approach could influence how utilities and regulators across the U.S. address cost allocation for AI-scale infrastructure, particularly as similar disputes intensify in constrained grid regions. Why 765-kV Transmission Signals Scale AEP says the

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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