Career Building Tips From A Senior Data Scientist At Amazon


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

This is a field that has exploded in popularity over the past decade and is expected to become 11.5 million More new jobs by 2026 in the US alone.

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 making the transition later in life)?

I asked Naveed Ahmed JanvekarA senior data scientist from Seattle who works on Amazon’s fraud and abuse prevention team to share her career journey.

Check out his story and the tips he has for people looking to pursue a data science career.

A spark: Using machine learning to solve real-world problems

What inspired you to pursue 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 eager to explore this area. So I began analyzing my personal financial transactions to generate trends and insights.

This has led to more time spent researching machine learning and how it can be used to our advantage to predict future outcomes and solve critical problems at large.

In order to gain better expertise in this field, I decided to pursue a Master’s degree in Informatics with a specialization in Machine Learning and Analytics.

After post-graduation, I worked at various US-based companies in various analytical roles, such as Analyst at Nanigans (a Boston-based edtech startup), Business Intelligence Developer at KPMG, and Senior Data Scientist at Amazon.

The role of AI in data security

What is the role of machine learning in your work as a Senior Data Scientist at Amazon?

Naveed Janvekar: Machine learning and data science have played an important role in my work 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 achieve scalability and higher precision than traditional rule-based and/or heuristic-based abuse detection.

As abuse behavior becomes complex over time, machine learning helps us tackle this challenge as we continually re-train the model with the latest abuse behavior/patterns.

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

communicating data-driven insights

What unexpected skills or experiences have helped you as a data science professional?

Naveed Janvekar: The skills to acquire domain expertise and be able to effectively and simply communicate insights to business stakeholders have helped me the most as a data science professional.

When I started my data science journey, I put a lot more emphasis on technical details than having a effective storyteller,

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

Working with Algorithms to Create Change

How should enterprises adapt their approach in this area going forward?

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

If you notice that a certain pattern appears repeatedly over time, you can create a business rule to flag the same pattern in the future.

However, this is a short-term solution. It does not keep up with the development of cheating patterns.

That’s where machine learning and AI come and go landscape changed,

Now, models are trained using historical data on many cheating behaviors, making these models robust and helping algorithms learn complex behaviors, which are more difficult for humans to do.

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

This helps automate actions resulting from model outputs, rather than requiring human auditors to evaluate suspicious entities that have been flagged after the fact.

Working with data and algorithms can be challenging

But what makes it exciting and fun?

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

Depending on domain expertise, data scientists can combine data in different ways to answer questions from business stakeholders, perform exploratory data analysis, find correlations between variables and features for improved model performance. Can conduct engineering.

With regards to algorithms, I’ve always experimented with different types of training on training datasets, conducting evaluations and taking a deep dive to find some algorithms work better than others.

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

All of this keeps work fun and exciting for me.

Be a part of the data science community

What is a useful tip you would like to share with data science beginners who are interested in marketing and its applications in commerce and want to upskill themselves in this field?

Naveed Janvekar: A useful suggestion would be to participate in research and inventions within machine learning and data science Workspace.

Be part of working groups that are attempting to use machine learning to solve problems in your area of ​​interest.

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 upskill 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 query comprehension, voice search and personalization.

Data scientists can explore applying various state-of-the-art algorithms to SEO use cases in order to measure the efficacy of new algorithms.

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

There are various magazines and conferences, such as IEEE International ConferenceOn Machine Learning and Applications to help you learn more about the latest Machine Learning trends.

This is not directly related to SEO, but will help you understand the technological advancements that will further hinder your placement.

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Featured image: Courtesy of Naved Janvekar





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