What it takes to be a decision scientist

Anya starts off the week by spending half a day with Rolls-Royce’s most important people: the customers.

She makes it a point to visit customers on-site once or twice a week, together with other data scientists and team members. This allows Anya and her colleagues to understand their customer’s needs better, particularly in areas of operational process and fuel efficiency. For instance, they need to help identify certain issues, such as fuel burn patterns, that might lead to additional costs.

She attends and helps drive an innovation roadmap discussion with her fellow business analysts and data scientists. It is a comprehensive session to work on an actionable plan that covers the entire problem-to-solution cycle—from identifying gaps to prescribing solutions, developing prototypes, testing and trials and implementation.

Together, they work out plans to tackle a client issue or adjust to changing situations. To design user-centric solutions, the team applies methodologies such as design thinking to solve complex problems and discover results-oriented solutions.

She has another innovation cell catch-up, this time with an aerospace consultant. Over the course of her work, Anya frequently reaches out to different domain experts.

Consultants bring domain knowledge to the table, helping Anya to contextualise the data’s relationship to the operation. They help draw up common scenarios for insights and visualisations, creating the “sounding board” for Anya to test her hypotheses. 

Anya heads up another innovation roadmap discussion. It’s important to get together frequently to benchmark progress and identify any issues early on. The technology landscape, especially for aviation, is a dynamic one that requires a great deal of forward planning. A business or technological need today might change due to evolving market conditions, and it is important to make the right decision today to ensure success tomorrow.

Over the course of the discussion, the team members are aware of new evolving challenges amid a fast-changing landscape such as aviation digital services. Regular discussions enable all team members to be aligned to the same goals, updated with the latest timeline and actionable steps to bring the project to success.

Anya pays another visit to a customer’s office. She presents findings that will help the client understand the issues at hand. With supporting evidence from the data shared during her discussions with co-workers, she proposes possible theories. Together, she works with the customers to co-create a solution to move forward. A collaborative approach means that the eventual solution would make a real business impact.

After the end of a long work week, Anya heads off and winds down with a session of yoga.

1. What is the key difference between a decision scientist and a data scientist?

Data scientists work directly with data and produce analytics they develop. Meanwhile, decision scientists like myself help to make that link between models the data scientist creates—and what the user needs to know—to make valuable decisions. That means taking the analytics outputs and generating insights, communicating and building a decision support framework.

In this type of role, I need to understand what the data scientist is doing and the performance of the models. It is important to be flexible in your approach and adapt your thinking for different stakeholders—from business to technological partners.

2. What got you interested in pursuing a science and technology career?

Growing up, I naturally excelled in mathematics and music. In high school, I became very active in extracurricular activities, which taught me the value of teamwork.  I spent most of my time studying, running cross-country, marching in band camp and playing in jazz and symphonic ensembles.

I was fortunate to go to an awesome school where almost everybody was a band nerd, athlete, artist or very active in giving to the community.  Teamwork and team spirit resonated everywhere and really helped me prepare for my future.

3. What were your main challenges making the transition from the academic to the business world?

In academia, I was accustomed to diving deep into a subject and spending a lot of time thinking of every creative solution possible.  I carried this habit to industry and learned very quickly that I don’t need to understand “everything” in so much detail. Instead, I can collaborate with other domain experts to tap on their knowledge and solve problems collectively.  Usually, the solution is more elegant and relevant as a result.

4. How did you overcome these hurdles?

Have a go-getting spirit! It’s important to have the courage to just dive in and figure it out.

Networking helps a lot too. Exchanging ideas with product managers, business analysts, scientists, engineers, customers and domain specialists helps you attune to Rolls-Royce’s business objectives.

5. Why did you choose Rolls-Royce instead of the younger technology companies? 

I wanted to work for an innovative and traditional engineering company that made awesome solutions—and that that pretty much sums up Rolls-Royce. 

6. You mentioned that addressing customer challenges excites you. Could you elaborate a few instances of project successes you were particularly proud of? 

One of the general successes and most fulfilling parts of the job is being able to show the customers innovative analytics and visualisation of their own data. It is thrilling to find out that your investigations are spot on, helping them confirm their hypothesis and automate plans they have had to optimise their operation.

7. What are some of the things you would like to achieve in your time at Rolls-Royce?

It’s to make a tangible and valuable contribution that I can be proud of. Also, I’d like to have been a significant inspiration or role model that would lead others in Rolls-Royce to continue to make their own valuable contributions.