7 Questions to Ask Before Taking on a Data Science Role

Most of us seasoned Data Scientists have been in the situation where we get excited by a new role, seemingly offering new challenges and learning opportunities, only to be disappointed soon after we begin.

We quickly realise that we can’t easily get access to the data we need, that it’s being guarded like some treasure, and that rather than use our skills and experience to solve complex problems, and build cool new models, we end up either churning out reports or battling the bureaucracy to prove the value and power of data driven decision making.

Here are some questions to answer before venturing into a new role, to hopefully give you a chance to make a real difference, and have fun and learn along the way:

  1. Availability and access to data? – Simply put, without data, we don’t exist! Try find out how easy/difficult it will be to get access to the data you’ll need, how long will it take to be granted access to the disparate systems it’s stored on, will you be able to speak directly to the data custodians to learn about the business, how is external data ingested ie regular/automatic feeds, web scraping etc…
  2. Is it just “vanity” Data Science? – Does the role exist to serve a real purpose, linked to the strategic goals of the organisation, or has it been created so they can jump on the Data Science bandwagon.
  3. Is it more than a reporting function? – Reporting is an integral part of the decision making process for senior management, but most Data Scientists yearn for more than just report creation. The automation of reports and creation of interactive dashboards is an obvious extension, and can be an interesting task, but if you want to learn and grow as a Data Scientist, you’ll want to at least dabble in other areas, such as developing predictive models, using Natural Language Processing to uncover insights from free text and social media feeds etc…
  4. Is the manager data literate? Are they empowered to make decisions? – Ideally, you want your manager to be a supporter, influencer and evangelist for what you can do and offer the organisation, someone who can help put the insights you discover into action, rather than someone who just talks about it. Try determine if they simply see themselves as an ‘Instant Expert’ in the exciting field of Data Science, and whether or nor they’re an actual decision maker.
  5. Does the capability already exist? Is it needed? What’s the perception of analytics and Data Science within the organisation? – Try determine if analytics is already being used in the organisation to drive change, has it been used before but failed to add significant value, or is it a new approach to genuinely try help make a difference. This will help assess the analytics maturity of the organisation.
  6. What models are already in production, if any? – This will also help assess the analytics maturity of the organisation, and determine whether or not they’re trying to solve problems at an enterprise level. It will also give you an idea of the processes they have in place for creating and embedding models in production systems, which teams are involved in the pipeline, and how advanced some of their capabilities, systems and people are.
  7. Is there senior management support? Do they use insights from analytics for decision making? – Ultimately, without being able to turn your insights into actionable decisions, it’s just an academic exercise. To really be able to make a difference, develop your profile and grow your career, you want the powers that be to use your insights for decision making. To do this, you need to show how Data Science can help make decision making better!

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