Overview: Career advice in Practice
This article from Revology Analytics explains career advice in the context of modern pricing analytics and revenue growth management. It draws on real engagements with mid-market and enterprise clients to turn career advice from a buzzword into a measurable commercial capability. Read on for the full perspective, and see our related reading for additional depth.
Table of Contents
Addressing the three most significant challenges to maximize career satisfaction and business impact
Data science, AI, and ML have been overhyped for at least the last decade, resulting in often unrealistic and misaligned expectations between data scientists and employers.
The above has been one of the fundamental drivers behind the often-cited phenomenon that ~85% of analytics and AI projects fail. Data scientists want to work on meaningful, intellectually satisfying, and challenging projects, hoping to finally capitalize on that hard-earned Ph.D. or Masters in a relevant domain. At the same time, companies often expect data science to be the panacea for foundational business problems, BHAG-type transformational challenges, or both. It often results in otherwise highly skilled and motivated data scientists, managers, and executives experiencing three primary obstacles early in their careers:
The mismatch between aspirations and reality:
Data scientists are doing lower-level and often rote work, more akin to what a data analyst or specialist tends to do. It tends to be the case at companies with a relatively low capability in managing their data assets and platforms, relegating data scientists to play the dual role of data engineer and BI specialist.
Example: VP of Sales routinely asks the Data Science COE for an Excel pivot table that summarizes Year-over-Year sales and gross profit results, overlayed with competitive price indexes. It is helpful information, but the organization would be better served if the request is routed to a data analyst or BI team.
Focusing on solutions, not problems:
Data scientists are unable to drive measurable value for their companies. More junior data scientists or those in the early part of their careers may focus more on model sophistication or solution accuracy instead of understanding and solving real problems that their stakeholders care about.
Example: VP of Marketing asks the Advanced Analytics team how competitive price changes and discounting practices impact the demand for the company’s anchor products. The analysis will be a key, albeit high-level, input to a price and promotional planning meeting that is taking place in three weeks. The data science team spends 2.6 weeks creating complex RNN models, using LIME for model interpretability, and developing a robust scenario analysis tool. They show the completed work to the VP of Marketing two days before the promotional planning session, which is left scratching her head after the meeting on how exactly to use this work.
The CXO “AI/ML fallacy”:
Data scientists sometimes cannot deliver on unreasonably high and ill-defined expectations by senior leadership. The same senior leaders may not yet be ML-literate, or they overestimate the current boundaries of AI/ML and underestimate the human factor needed for enterprise-wide AI/solutions to add value.
Industry buzz about AI and the Fourth Industrial Revolution, along with a proliferation of white papers and vendor solutions, have created welcome excitement about the power of analytics and data science amongst CEOs and CXOs. However, it has also resulted in misplaced corporate expectations about just what exactly AI/ML can do for their companies.
Example: a data scientist is tasked with building a semi-autonomous digital control tower for a multi-billion-dollar enterprise that will optimize all pricing, procurement, and supply chain decisions with minimal human involvement.
While possible in theory, it is nearly impossible to successfully execute as a holistic, integrated solution platform unless you are a company whose core product is AI/ML in the truest sense (as opposed to “AI/ML” companies that do stats).
Over the years, I have seen, experienced, and led more analytics project failures than successes, which is the reality for most seasoned analytics practitioners. Experienced data scientists have learned by now that the challenges to productionalizing models, driving user adoption of analytics products, or driving measurable business impact are rarely solved through more robust technical or algorithmic capabilities. Solid data science foundations are a must-have, but traditionally softer skills like business operator empathy, stakeholder management, and being problem-oriented and results-focused are more critical success factors to succeeding in the analytics game in the long run.
In a subsequent article, we will dive deeper into each of the three realities above, understand why these problems exist, and give specific and actionable career advice to aspiring or nascent data science and analytics practitioners on how best to navigate them for a meaningful and impactful career.
Frequently asked questions about career advice for data scientists
What are the biggest career challenges for data scientists?
Three obstacles show up early in many data science careers: a mismatch between aspirations and the work companies actually need, a focus on solutions instead of the problems stakeholders care about, and the CXO AI/ML fallacy, where senior leaders set unreasonably high and ill-defined expectations. All three stem from a decade of hype that left data scientists and employers with misaligned expectations.
Why do so many analytics and AI projects fail?
An often-cited figure says about 85% of analytics and AI projects fail, and misaligned expectations are a fundamental driver. Data scientists want meaningful, challenging projects, while companies often expect data science to cure foundational business problems or deliver sweeping transformations. The hard parts, such as getting models into production, driving user adoption, and proving business impact, are rarely solved with stronger technical or algorithmic capabilities.
Why do data scientists end up doing data analyst work?
It happens most at companies with weak capabilities for managing their data assets and platforms, where data scientists end up doubling as data engineers and BI specialists. A typical case is a VP of Sales asking the data science team for an Excel pivot table of year-over-year sales and gross profit with competitive price indexes. The information is useful, but a data analyst or BI team should handle that request.
What is the CXO AI/ML fallacy?
It is the gap between what senior leaders expect from AI and machine learning and what the technology can deliver. Leaders who are not yet ML-literate often overestimate its boundaries and underestimate the human effort needed to make enterprise-wide solutions work. Asking a data scientist to build a semi-autonomous control tower that runs all pricing, procurement, and supply chain decisions is a typical example, and nearly impossible unless AI is the company’s core product.
How can data scientists deliver more business impact?
Start from the problem your stakeholders care about, not from model sophistication or accuracy. Solid data science foundations are a must-have, but softer skills decide long-term success: empathy for business operators, stakeholder management, and a problem-oriented, results-focused mindset. In one case, a team spent 2.6 weeks of a three-week window on RNN models and a scenario tool, and the marketing VP was left unsure how to use it.
Related Reading
- The Sinking Feeling of Weak Pricing Power? BATNA is Your Anchor
- Taking Charge of Your Revenue Growth Analytics
For broader industry perspective on pricing analytics and revenue growth management, see McKinsey’s Growth, Marketing & Sales insights.
