Beyond the Algorithm: Why Managing Up is Your Most Important Data Science Skill
Article by John Tribbia
I’ve been wrangling data since before we called it “data science.” Back then, finding someone else in the company who spoke ‘data’ was sometimes like finding a unicorn. In those early days, I was the data science department – a lone translator trying to bridge the gap between complex analysis and business decisions.
It was a trial by fire, but it taught me a crucial lesson that fancy algorithms and perfect models alone can’t teach: your technical brilliance means little if you can’t communicate its value and guide its application. That’s where “managing up” comes in.
Drawing upon my experiences as a National Outdoor Leadership School (NOLS) graduate and passionate outdoors enthusiast, I often conceptualize leadership using the framework of wilderness expeditions. This involves understanding different roles such as Designated Leaders, Active Followers, Peer Leaders, and Self Leaders. In the wilderness, survival and success depend on everyone understanding their role and proactively communicating risks, solutions, and needs. A breakdown in communication can mean getting lost, running out of supplies, or worse.
In the “wilderness” of corporate data science, the stakes might seem lower, but the principle is identical. Managing up isn’t about sucking up or office politics. It’s the strategic communication and proactive engagement required to ensure your hard work actually makes an impact. It’s the missing link that connects all those leadership roles, especially when you’re the technical expert trying to navigate complex organizational terrain.
Why Managing Up is Non-Negotiable for Data Scientists (Especially the Lone Wolves) Data science is inherently complex. We deal in probabilities, uncertainty, and models that can feel like black boxes to our non-technical colleagues and leaders. When I was the only data person, I quickly learned:
- You Need Buy-In: Getting resources (time, compute power, access to more data) requires convincing leadership that your work is valuable and aligned with business goals. You can’t just show code; you have to sell the potential outcome.
- Translation is Key: You are the bridge. You have to translate business problems into data problems and, crucially, translate your findings back into actionable business insights. Waiting to be asked the right questions often means waiting forever.
- Proactivity Prevents Pain: Stakeholders outside your immediate team often impact your work (data quality issues, changing requirements). Spotting these dependencies and flagging them early saves massive headaches later. As the lone data voice, no one else was likely to spot these technical roadblocks.
Shifting from Problem-Spotter to Solution-Driver
Too many brilliant technical minds fall into the trap of just presenting problems. “The model accuracy dropped.” “The data pipeline is broken.” “This analysis is complex.”
Your manager, often juggling multiple priorities and lacking your deep technical context, hears this and thinks, “Okay… now what?”
Managing up means shifting your mindset. That is, assume the burden of outlining solutions, not just presenting problems.
Instead of: “Here’s a complex dataset, what should I do?” Try: “I’ve analyzed the dataset. Here’s a summary visualization highlighting [key trend]. Based on this, I propose we investigate either [Option A] or [Option B] next. Do you agree, or is there other context I should consider?”
This proactive approach instantly demonstrates critical thinking, ownership, and respect for your manager’s time.
The Four Pillars of Managing Up (Data Science Edition)
- Focus on the Punchline: Lead with the conclusion. Then provide context. Your manager might only have 30 seconds between meetings.
Example: “Our new churn prediction model identifies 15% higher risk in Segment X, suggesting an immediate targeted retention campaign. The key drivers are…” (Details follow, but the core message is upfront).
- Show Your Thought Process: Briefly explain why you reached your conclusion or recommendation. This builds trust and allows for constructive feedback.
Example: “…I explored features A, B, and C. Feature B showed the strongest correlation after accounting for [confounding variable]. This aligns with the user feedback we saw last quarter.”
- Anticipate and Flag Issues: Don’t wait for things to break. Proactively identify potential roadblocks and their impact.
Example: “We’re on track for the dashboard launch, but I’m seeing data latency from Source Y increasing. If this continues, insights could be delayed by 12 hours. Should we prioritize investigating this or accept the potential delay?”
- Solutions, Not Complaints: Frame challenges constructively. Offer potential paths forward, even if they aren’t perfect. Remember the quote: “Don’t come to me with just problems, come to me with problems and solutions.”
Example: “The data quality for feature Z is lower than expected, impacting model performance. We could either:
a) spend ~2 days cleaning it,
b) drop the feature temporarily, or
c) proceed with caution, noting the limitation.
I recommend ‘a’ for long-term accuracy. What are your thoughts?”
It’s Not Just Upward – It’s Everywhere
While we call it “managing up,” these principles apply laterally (to peers, collaborators) and even downwards (if you mentor or lead). It’s about clear, proactive, solution-oriented communication tailored to your audience. When I was the sole data person, my “peers” were often marketing managers, product managers, or engineers, and my “manager” was the Director of Marketing who needed the ‘so what’ more than the ‘how.’
The Takeaway
Effective communication and managing up aren’t soft skills; they are essential skills for impact in data science. They ensure your insights land, your models get deployed, and your work drives real business value. It’s how you move from being a brilliant technician to a trusted strategic partner.
Whether you’re navigating the corporate structure like a wilderness expedition or you are the lone scout, embracing these principles will make you more effective, influential, and ultimately, more successful in your career. As Peter Drucker said, “Management is doing things right; leadership is doing the right things.” Managing up helps ensure you, and your team, are focused on doing the right things. Start practicing today. Be explicit about what you need, anticipate questions, and always connect your work back to the bigger picture. Your manager, your stakeholders, and your future self will thank you.