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Career Building Tips From A Senior Data Scientist At Amazon

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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.

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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?

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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?

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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.

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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?

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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?

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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?

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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.

More Resources:

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Featured Image: Courtesy of Naveed Janvekar




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Google Clarifies Vacation Rental Structured Data

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Google updates their vacation rental structured data documentation

Google’s structured data documentation for vacation rentals was recently updated to require more specific data in a change that is more of a clarification than it is a change in requirements. This change was made without any formal announcement or notation in the developer pages changelog.

Vacation Rentals Structured Data

These specific structured data types makes vacation rental information eligible for rich results that are specific to these kinds of rentals. However it’s not available to all websites. Vacation rental owners are required to be connected to a Google Technical Account Manager and have access to the Google Hotel Center platform.

VacationRental Structured Data Type Definitions

The primary changes were made to the structured data property type definitions where Google defines what the required and recommended property types are.

The changes to the documentation is in the section governing the Recommended properties and represents a clarification of the recommendations rather than a change in what Google requires.

The primary changes were made to the structured data type definitions where Google defines what the required and recommended property types are.

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The changes to the documentation is in the section governing the Recommended properties and represents a clarification of the recommendations rather than a change in what Google requires.

Address Schema.org property

This is a subtle change but it’s important because it now represents a recommendation that requires more precise data.

This is what was recommended before:

“streetAddress”: “1600 Amphitheatre Pkwy.”

This is what it now recommends:

“streetAddress”: “1600 Amphitheatre Pkwy, Unit 6E”

Address Property Change Description

The most substantial change is to the description of what the “address” property is, becoming more descriptive and precise about what is recommended.

The description before the change:

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PostalAddress
Information about the street address of the listing. Include all properties that apply to your country.

The description after the change:

PostalAddress
The full, physical location of the vacation rental.
Provide the street address, city, state or region, and postal code for the vacation rental. If applicable, provide the unit or apartment number.
Note that P.O. boxes or other mailing-only addresses are not considered full, physical addresses.

This is repeated in the section for address.streetAddress property

This is what it recommended before:

address.streetAddress Text
The full street address of your vacation listing.

And this is what it recommends now:

address.streetAddress Text
The full street address of your vacation listing, including the unit or apartment number if applicable.

Clarification And Not A Change

Although these updates don’t represent a change in Google’s guidance they are nonetheless important because they offer clearer guidance with less ambiguity as to what is recommended.

Read the updated structured data guidance:

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Vacation rental (VacationRental) structured data

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Google On Hyphens In Domain Names

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What Google says about using hyphens in domain names

Google’s John Mueller answered a question on Reddit about why people don’t use hyphens with domains and if there was something to be concerned about that they were missing.

Domain Names With Hyphens For SEO

I’ve been working online for 25 years and I remember when using hyphens in domains was something that affiliates did for SEO when Google was still influenced by keywords in the domain, URL, and basically keywords anywhere on the webpage. It wasn’t something that everyone did, it was mainly something that was popular with some affiliate marketers.

Another reason for choosing domain names with keywords in them was that site visitors tended to convert at a higher rate because the keywords essentially prequalified the site visitor. I know from experience how useful two-keyword domains (and one word domain names) are for conversions, as long as they didn’t have hyphens in them.

A consideration that caused hyphenated domain names to fall out of favor is that they have an untrustworthy appearance and that can work against conversion rates because trustworthiness is an important factor for conversions.

Lastly, hyphenated domain names look tacky. Why go with tacky when a brandable domain is easier for building trust and conversions?

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Domain Name Question Asked On Reddit

This is the question asked on Reddit:

“Why don’t people use a lot of domains with hyphens? Is there something concerning about it? I understand when you tell it out loud people make miss hyphen in search.”

And this is Mueller’s response:

“It used to be that domain names with a lot of hyphens were considered (by users? or by SEOs assuming users would? it’s been a while) to be less serious – since they could imply that you weren’t able to get the domain name with fewer hyphens. Nowadays there are a lot of top-level-domains so it’s less of a thing.

My main recommendation is to pick something for the long run (assuming that’s what you’re aiming for), and not to be overly keyword focused (because life is too short to box yourself into a corner – make good things, course-correct over time, don’t let a domain-name limit what you do online). The web is full of awkward, keyword-focused short-lived low-effort takes made for SEO — make something truly awesome that people will ask for by name. If that takes a hyphen in the name – go for it.”

Pick A Domain Name That Can Grow

Mueller is right about picking a domain name that won’t lock your site into one topic. When a site grows in popularity the natural growth path is to expand the range of topics the site coves. But that’s hard to do when the domain is locked into one rigid keyword phrase. That’s one of the downsides of picking a “Best + keyword + reviews” domain, too. Those domains can’t grow bigger and look tacky, too.

That’s why I’ve always recommended brandable domains that are memorable and encourage trust in some way.

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Read the post on Reddit:

Are domains with hyphens bad?

Read Mueller’s response here.

Featured Image by Shutterstock/Benny Marty

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Reddit Post Ranks On Google In 5 Minutes

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Google apparently ranks Reddit posts within minutes

Google’s Danny Sullivan disputed the assertions made in a Reddit discussion that Google is showing a preference for Reddit in the search results. But a Redditor’s example proves that it’s possible for a Reddit post to rank in the top ten of the search results within minutes and to actually improve rankings to position #2 a week later.

Discussion About Google Showing Preference To Reddit

A Redditor (gronetwork) complained that Google is sending so many visitors to Reddit that the server is struggling with the load and shared an example that proved that it can only take minutes for a Reddit post to rank in the top ten.

That post was part of a 79 post Reddit thread where many in the r/SEO subreddit were complaining about Google allegedly giving too much preference to Reddit over legit sites.

The person who did the test (gronetwork) wrote:

“…The website is already cracking (server down, double posts, comments not showing) because there are too many visitors.

…It only takes few minutes (you can test it) for a post on Reddit to appear in the top ten results of Google with keywords related to the post’s title… (while I have to wait months for an article on my site to be referenced). Do the math, the whole world is going to spam here. The loop is completed.”

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Reddit Post Ranked Within Minutes

Another Redditor asked if they had tested if it takes “a few minutes” to rank in the top ten and gronetwork answered that they had tested it with a post titled, Google SGE Review.

gronetwork posted:

“Yes, I have created for example a post named “Google SGE Review” previously. After less than 5 minutes it was ranked 8th for Google SGE Review (no quotes). Just after Washingtonpost.com, 6 authoritative SEO websites and Google.com’s overview page for SGE (Search Generative Experience). It is ranked third for SGE Review.”

It’s true, not only does that specific post (Google SGE Review) rank in the top 10, the post started out in position 8 and it actually improved ranking, currently listed beneath the number one result for the search query “SGE Review”.

Screenshot Of Reddit Post That Ranked Within Minutes

Anecdotes Versus Anecdotes

Okay, the above is just one anecdote. But it’s a heck of an anecdote because it proves that it’s possible for a Reddit post to rank within minutes and get stuck in the top of the search results over other possibly more authoritative websites.

hankschrader79 shared that Reddit posts outrank Toyota Tacoma forums for a phrase related to mods for that truck.

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Google’s Danny Sullivan responded to that post and the entire discussion to dispute that Reddit is not always prioritized over other forums.

Danny wrote:

“Reddit is not always prioritized over other forums. [super vhs to mac adapter] I did this week, it goes Apple Support Community, MacRumors Forum and further down, there’s Reddit. I also did [kumo cloud not working setup 5ghz] recently (it’s a nightmare) and it was the Netgear community, the SmartThings Community, GreenBuildingAdvisor before Reddit. Related to that was [disable 5g airport] which has Apple Support Community above Reddit. [how to open an 8 track tape] — really, it was the YouTube videos that helped me most, but it’s the Tapeheads community that comes before Reddit.

In your example for [toyota tacoma], I don’t even get Reddit in the top results. I get Toyota, Car & Driver, Wikipedia, Toyota again, three YouTube videos from different creators (not Toyota), Edmunds, a Top Stories unit. No Reddit, which doesn’t really support the notion of always wanting to drive traffic just to Reddit.

If I guess at the more specific query you might have done, maybe [overland mods for toyota tacoma], I get a YouTube video first, then Reddit, then Tacoma World at third — not near the bottom. So yes, Reddit is higher for that query — but it’s not first. It’s also not always first. And sometimes, it’s not even showing at all.”

hankschrader79 conceded that they were generalizing when they wrote that Google always prioritized Reddit. But they also insisted that that didn’t diminish what they said is a fact that Google’s “prioritization” forum content has benefitted Reddit more than actual forums.

Why Is The Reddit Post Ranked So High?

It’s possible that Google “tested” that Reddit post in position 8 within minutes and that user interaction signals indicated to Google’s algorithms that users prefer to see that Reddit post. If that’s the case then it’s not a matter of Google showing preference to Reddit post but rather it’s users that are showing the preference and the algorithm is responding to those preferences.

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Nevertheless, an argument can be made that user preferences for Reddit can be a manifestation of Familiarity Bias. Familiarity Bias is when people show a preference for things that are familiar to them. If a person is familiar with a brand because of all the advertising they were exposed to then they may show a bias for the brand products over unfamiliar brands.

Users who are familiar with Reddit may choose Reddit because they don’t know the other sites in the search results or because they have a bias that Google ranks spammy and optimized websites and feel safer reading Reddit.

Google may be picking up on those user interaction signals that indicate a preference and satisfaction with the Reddit results but those results may simply be biases and not an indication that Reddit is trustworthy and authoritative.

Is Reddit Benefiting From A Self-Reinforcing Feedback Loop?

It may very well be that Google’s decision to prioritize user generated content may have started a self-reinforcing pattern that draws users in to Reddit through the search results and because the answers seem plausible those users start to prefer Reddit results. When they’re exposed to more Reddit posts their familiarity bias kicks in and they start to show a preference for Reddit. So what could be happening is that the users and Google’s algorithm are creating a self-reinforcing feedback loop.

Is it possible that Google’s decision to show more user generated content has kicked off a cycle where more users are exposed to Reddit which then feeds back into Google’s algorithm which in turn increases Reddit visibility, regardless of lack of expertise and authoritativeness?

Featured Image by Shutterstock/Kues

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