Taking a New Job in Analytics? Ask These 5 Questions First

ByJordan Brown

Congratulations, you‘ve landed an interview for an exciting new analytics role! While it‘s tempting to jump at the opportunity, it‘s critical to do your due diligence and ensure the position is truly the right fit for your skills, interests and career goals. As an AI and machine learning leader who has built teams and evaluated hundreds of candidates, I‘ve seen the difference that probing questions can make in identifying an analytics job that will accelerate your professional development vs. one that may stall your progress.

Asking thoughtful questions shows that you‘re discerning and serious about your career. It helps you cut through the hype to understand the real nature of the work, the expectations of the role, and the opportunities for growth. And it sets you up to hit the ground running if you do decide to accept the offer.

While there are many factors to explore, here are the top 5 questions I recommend asking before signing on the dotted line for an analytics role:

1. What is the mix of analysis vs. reporting responsibilities?

Not all "analytics" roles are created equal. Some are heavily focused on reporting – compiling data into a preset format, updating dashboards, and providing basic commentary on trends. Others are truly analytical – identifying business problems, designing experiments, digging into the data to uncover insights, and delivering recommendations. Many fall somewhere in between.

Understanding where the role lands on this spectrum is key to evaluating fit. If you‘re eager to hone your statistical chops and drive business decisions, a reporting-heavy role may not be the best match. On the flip side, if you‘re looking to build foundational data skills and learn the business, a role with some reporting responsibilities could be a great stepping stone.

Push for specifics on the types of projects you‘d be working on and the methods you‘d be using. For AI/ML roles in particular, ask about the balance between hands-on model building vs. stakeholder communication and result interpretation. Strong data science teams recognize the importance of both sides of the equation.

According to the 2020 Anaconda State of Data Science survey, the top activities data scientists spend their time on are:

  • Data preparation and cleansing (45%)
  • Building AI/ML models (42%)
  • Data visualization and reporting (40%)
  • Research and development (33%)
  • Production model deployment (26%)

Look for opportunities that will allow you to grow across multiple dimensions, not pigeonhole you into one narrow function.

2. What real-world examples can you share of analyses driving impact?

To get a concrete sense of the work, ask your interviewers to walk you through a recent project that demonstrates the team‘s impact. Pay attention to the level of sophistication of the analysis, the cross-functional collaboration involved, and the tangible outcomes achieved.

A strong answer might sound like: "Last quarter, we partnered with the marketing team to optimize our digital ad spend. We analyzed data from 20+ campaigns across 5 channels to build a multi-touch attribution model that identified the most effective channel mix. We ran an experiment to validate the model‘s recommendations, which drove a 15% increase in conversions and a 10% decrease in cost per acquisition. Based on these results, the marketing team is rolling out the new channel mix globally and estimates it will deliver $2M in incremental revenue over the next year."

In contrast, a less compelling answer might be: "Each week, we pull data on marketing campaign performance and send a report to the team with the top 10 campaigns by spend and the bottom 10 campaigns by click-through rate. The team uses this to inform their optimization efforts." While useful, this type of reporting is more descriptive than prescriptive.

For AI/ML roles, dig into how models are making it into production and delivering real value. Gartner estimates that 85% of AI projects will "deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them" through 2022. Push to understand how the team is mitigating these risks and ensuring responsible, effective deployment of models.

3. How is the analytics team structured and what is the leadership‘s vision?

Analytics teams can roll up to a variety of functions – from IT to finance to operations to a standalone unit reporting to the CEO. Understanding the organizational structure and leadership‘s vision for analytics can tell you a lot about the level of influence and executive buy-in the team has.

In a setup where analytics is buried within IT and viewed as a "service" function, you may have limited exposure to business partners and fewer opportunities to shape strategy. When analytics has a seat at the executive table and is seen as a driver of innovation, the inverse is often true.

Ask about the backgrounds of the analytics leaders and how they partner with other executives to set priorities. In high-performing organizations, the analytics leader is a key member of the senior team and works hand-in-hand with business leads to identify high-impact use cases and deliver measurable value.

Also explore what mechanisms exist to share learnings and best practices across the analytics community. Is there a regular speaker series or tech talk? Hackathons or team demo days? Informal coffee chats or Slack channels? The best analytics organizations foster ongoing learning and collaboration.

For AI/ML roles, ask if the company has an AI Center of Excellence (CoE) or similar governance body. AI CoEs are increasingly common – 70% of companies surveyed by Accenture in 2021 have one in place. They can play a valuable role in aligning AI/ML initiatives to business priorities, developing standards and guidance, and communicating with stakeholders.

4. What opportunities exist for skills development and career growth?

Analytics is a fast-moving field, with new techniques and technologies emerging all the time. Staying current and continuously expanding your skillset is critical to long-term career success. Look for roles that offer ample opportunity to learn and grow, both through formal training and on-the-job experiences.

Ask what resources are available for skills development – things like conference and workshop budgets, online course subscriptions, tuition reimbursement, dedicated training days, mentoring programs, etc. The best companies view these as investments, not expenses.

Dig into what career paths look like for analysts at the company. Is there a clearly defined job ladder with increasing levels of responsibility? Opportunities to rotate across different teams or business units? Support for pursuing advanced degrees or professional certifications?

In the MIT Sloan Management Review and SAS report "The Changing Landscape of Data Science and Machine Learning", the top skills identified as critical for career advancement in analytics were:

  • Communication (85%)
  • Problem solving (85%)
  • Statistics (76%)
  • Technical skills (75%)
  • Business acumen (74%)

Look for roles that will allow you to hone both your technical abilities and your business/soft skills in equal measure. And don‘t be afraid to ask about promotion metrics and success stories – a strong analytics leader should be able to cite examples of team members who have grown into new roles and expanded their impact.

5. What tools and technologies does the team use – and what‘s the appetite for exploring new ones?

To be effective in an analytics role, you need access to the right tools for the job. While Excel can suffice for basic reporting and ad-hoc analysis, more sophisticated work often requires specialized business intelligence, data visualization, statistical analysis and model development software.

Ask about the team‘s "tech stack" – what tools they use for data preparation, storage, analysis, visualization, model training/deployment, etc. How modern and robust are the systems? Is there a mix of proprietary and open source technologies? How does the team stay on top of the latest innovations in tooling?

For AI/ML roles in particular, probe into what frameworks and infrastructure the team has in place to support the end-to-end model lifecycle – from prototyping to training to testing to deployment to monitoring. Is there a data science workbench or platform that allows for collaboration and reproducibility? MLOps tools to streamline model management?

Rapidminer‘s 2020 "State of Data Science and Machine Learning" report found that 39% of data scientists spend more than 40% of their time on infrastructure and tooling issues rather than actual analysis. The right setup can eliminate a lot of that toil.

Also gauge the team‘s appetite for continuous improvement and technology adoption. Is there a spirit of experimentation and calculated risk-taking? If an analyst sees a promising new tool or technique, is there a clear path to vet and potentially implement it? Or is the team content with the status quo? The best analytics organizations strike a balance between standardization and flexibility.

Putting It All Together

Asking these 5 questions – and carefully evaluating the answers – can help you suss out if an analytics role is right for you. But don‘t stop there. Use this as a starting point to probe into the areas that matter most to you in your next role, whether that‘s domain expertise, team culture, work-life balance, or social impact.

Remember, an interview is a two-way street. It‘s not just about selling yourself to the company, but also making sure the company is a good fit for your skills, interests and goals. By doing your homework and asking thoughtful questions, you‘ll be well on your way to finding an analytics role that challenges and fulfills you.

Additional Questions to Consider

Beyond the core 5 questions we‘ve explored in depth, here are a few other areas you may want to probe into as you evaluate an analytics opportunity:

  • What processes and best practices does the team have in place for data governance, security and privacy? This is especially critical in regulated industries like healthcare and financial services.
  • How does the team ensure quality and accuracy of analyses? What kind of testing, peer review and documentation is expected?
  • What opportunities exist to share work and thought leadership externally? Is publishing and speaking at conferences encouraged?
  • How is performance evaluated for analysts? What metrics are used and how often are reviews conducted?
  • For AI/ML roles: How is the team thinking about issues of fairness, accountability and transparency in their models? What processes are in place to identify and mitigate bias?

Resources for Further Reading

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