Career Building Tips From A Senior Data Scientist At Amazon

Data science as a discipline – and specific skills in machine learning, analytics, and training algorithms – are in hot demand.

It’s a field that has exploded in popularity this past decade and is expected to create 11.5 million more new jobs in the U.S. alone by 2026.

So what’s it like to work as a data scientist, and what do you need to know if you’re thinking of starting your career there (or transitioning in later in life)?

I asked Naveed Ahmed Janvekar, a Senior Data Scientist from Seattle who works in Amazon’s fraud and abuse prevention team, to share his career journey.

Check out his story and the tips he has for those interested in pursuing a data science career.

A Spark: Using Machine Learning To Solve Real-World Problems

What led you to a career in data science?

Naveed Janvekar: My interest in machine learning grew when I was working for Fidelity Investments as a Software Developer.

I had colleagues who were working as analysts with data to identify trends, which made me curious to explore this field. So I started analyzing my personal financial transactions to generate trends and insights.

This led to spending more time researching machine learning and how one could leverage it to model repetitive patterns to predict future outcomes and use it to our advantage to solve critical problems at scale.

In order to gain better expertise in this domain, I decided to pursue my Master’s in Information Science with a specialization in Machine Learning and Analytics.

Post-graduation, I worked at various U.S.-based companies in different analytical roles such as Analyst at Nanigans (a Boston-based AdTech startup), Business Intelligence Developer at KPMG, and Senior Data Scientist at Amazon.

The Role Of AI In Data Security

What role does machine learning play in your work as Sr. Data Scientist at Amazon?

Naveed Janvekar: Machine learning and data science play a vital role in my job at Amazon.

In the abuse prevention team, we use various classification algorithms and deep learning algorithms to detect fraud and abuse on the platform.

Machine learning helps with achieving scalability and high precision detection as compared to traditional rule-based and/or heuristic-based abuse detection.

As abuse behaviors get complex over time, machine learning helps us with this challenge since we constantly re-train models with the latest abuse behavior/patterns.

I have filed patents for inventions related to the detection of emerging abuse on the platform using machine learning.

Communicating Data-Driven Insights

What unexpected skill or experience do you feel has helped you as a data science professional?

Naveed Janvekar: The skill of gaining domain expertise and being able to effectively and simplistically communicate insights to business stakeholders has helped me the most as a data science professional.

When I began my data science journey, I put a lot more emphasis on technical details than being an effective storyteller.

But over the last few years, I’ve realized that being able to communicate narratives and insights from data science or machine learning is as important as implementing machine learning strategies.

Working Alongside Algorithms To Create Change

How should enterprises tailor their approach in this space moving forward?

Naveed Janvekar: In the past, fraud prevention was traditionally done using business heuristic rules.

If you observed a certain pattern appear frequently over time, you can put in a business rule to flag the same pattern in the future.

However, this is a short-term solution. It doesn’t keep up with the evolution of fraud patterns.

This is where machine learning and AI come in and have changed the landscape.

Now, models are trained using historical data across multiple behaviors of fraud, making these models robust and helping algorithms learn complex behavior, which is much more difficult for humans to do.

Enterprises have started using machine learning in fraud detection. They must now focus on aspects such as automated re-training of models to capture the latest behaviors in fraud and make models highly precise.

This helps automate actions as a result of model output, rather than having human auditors required to evaluate suspicious entities that are flagged after the fact.

Working With Data And Algorithms Can Be Challenging

But what makes it exciting and fun?

Naveed Janvekar: I’ve enjoyed feature engineering from data, which brings out my creative side.

Based on domain expertise, data scientists can munge the data in different ways to answer business stakeholders’ questions, perform exploratory data analysis, find correlations among variables, and conduct feature engineering for better model performances.

With respect to algorithms, I have always experimented with training different kinds on training datasets, conducting evaluations, and taking a deep dive into why certain algorithms work better than others.

This helps me gain a deeper understanding of these algorithms and situations where they work – and where they don’t.

All of this keeps the work fun and exciting for me.

Becoming A Part Of The Data Science Community

What’s one useful tip you’d want to share with data science beginners who are interested in its applications in marketing and commerce and may want to upskill themselves in this field?

Naveed Janvekar: One useful suggestion would be to participate in research and inventions within the machine learning and data science domain.

Be part of working groups that are trying to solve problems in your area of interest using machine learning.

Contribute to their research, get peer feedback, publish papers, and file patents.

Through these mechanisms, you are actively contributing to the science community, constantly learning from peers, and upskilling yourself.

It’s also a good idea to have a data science mentor.

Keeping Up With SEO Trends

How does a data scientist stay up-to-date and informed in the field of SEO?

Naveed Janvekar: In the field of SEO, machine learning helps with the understanding of queries, voice search, and personalization.

Data scientists can explore applying various state-of-the-art algorithms for SEO use cases to measure the efficacy of newer algorithms.

Doing this will keep data scientists up-to-date with the latest trends in the industry, as well as updating the machine learning stack in SEO-related firms.

There are various journals and conferences, such as the IEEE International Conference, on machine learning and applications to help you learn more about the latest machine learning trends.

It’s not directly SEO-related but will help you understand the technological advancements that will disrupt your space next.

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Featured Image: Courtesy of Naveed Janvekar