Data Scientist Seeking Feedback for Exceptional Talent

Stage 1
1

Hi All -

Happy New Year! I’ve been following this forum for a while and have decided to apply for Exceptional Talent. I would welcome your critical perspective on how to further solidify my arguments and ensure a seamless and high-impact case for endorsement.

I decide to go with MC + OC3 + OC4 and wanted to hear your thoughts.

Background:

  • Senior Data Scientist at a FAANG company (4.5+ years).
  • 10 YOE total: Previous experience was mostly quant trading.
  • Credentials: Certified FRM (Financial Risk Manager) and IEEE Senior Member.

Recommendation Letters (Main 3)

  • CTO of an external partner firm: To prove leadership on a collaborative product that is a high industry priority.
  • Senior DS Director at my current firm: To validate my leadership in product development and mentorship of the DS organization.
  • Associate Editor of a Q1 Journal: To prove my standing as a recognized expert/judge in the field (20+ manuscripts reviewed).

MC

  • High Remuneration & Performance: Evidence of top-tier salary, equity, and performance ratings.
  • Governance & Leadership: Member of an internal governance committee that audits and approves measurement frameworks for company-wide growth products.
  • Professional Seniority: IEEE Senior Member status and evidence of serving as an expert reviewer for a specialized IEEE journal.

OC3
For my current role, I am providing evidence of my significant contribution to three products that have delivered substantial commercial success. My role centered on defining the product strategies, measurement frameworks to define their success, and technical roadmaps that allowed these products to scale.

I can provide LOR, documentation, and media report (although my name was not mentioned) to verify my key contribution.

OC4

  • Publications: Two recent papers in journals with Q1 impact factors.
  • Conferences: Three papers accepted at IEEE conferences.
  • Expert Reviewer: 20+ peer reviews for high-impact journals/conferences.

@pahuja @Akash_Joshi directly and everyone who can kindly review. Thank you so much for your help!!!

2 Likes
2

also @Francisca_Chiedu @Raphael since it only allows to @ two people for new users.

3

Hi @iam1cent

You have a promising case esp wrt OC3 and OC4. However your MC is weak since salary evidence is anyway the lowest weight evidence for MC. IEEE pan reviewer is good but your internal governance evidence is invalid for MC. Instead pick a large company initiative you led that resulted in industry recognition like press coverage/award. OC3 need to clearly show your contribution and it’s quantified impact on core company metrics.

1 Like
4

Priyanka - Thank you so much for the super quick response! Regarding your suggestion on MC, I did work on a company level priority product as leading data scientist for its global launch. Although my name was not directly mentioned, there are many media coverage about this product and I can provide some internal and external evidence (e.g. one of the recommender) to prove my leadership. Do you think that is better to proof MC?

5

Yes this is much better since internal governance and processes aren’t valid for the application

1 Like
6

Hi @iam1cent,

You have come a long way academically, and working at a FAANG company (4.5+ years) shows that you also have industry impact. These give you a good narrative to start with.

Let’s start with you being a Senior Data Scientist, which is a technical role. Reading statements like “…my leadership in product development and…” tells me you are also positioning yourself as a Product Manager, which is a business role. This weakens your narrative. While being a T-shaped professional can be appreciated in the industry or in startup settings, it does not work well for a Tech Nation application.

The Associate Editor of a Q1 journal may not be suitable to recommend you unless the person is a well-established individual recognized as an expert in the digital technology field. Academics are not necessarily experts in the tech sector.

MC

Evidence of top-tier salary, equity, and performance ratings can be acceptable if backed up by other convincing evidence, because salary alone can be a result of location, company type, or sector, and does not by itself validate that it is due to one’s skills. Hence, Tech Nation guidelines state this is not sufficient on its own.

Governance & Leadership does not seem to show that you have been recognised as a leading talent in the digital technology sector, and it is also internal.

Being a reviewer for a specialized IEEE journal is good MC evidence if presented properly, but you need to merge this with OC4 (Expert Reviewer: 20+ peer reviews for high-impact journals/conferences). They are similar, and you need at least two unique pieces of evidence in one evidence set.

Your OC3 is weak. Statements like “my role centered on defining product strategies, measurement frameworks to define their success, and technical roadmaps” align more with a Product Manager job description. Even then, these are common responsibilities and do not show significant technical or commercial contribution. To make this worse, you stated that you are a Senior Data Scientist, not a Product Manager.

OC4

Two recent papers in journals with Q1 impact factors are good, but because they are recent, they will not show substantial contributions in terms of citations, references, readership, or engagement. Contribution is the major factor for this criterion, not just publication.

Three papers accepted at IEEE conferences are good, but did you speak at the conferences? Were the papers published? Beyond acceptance, these are the contribution elements that matter.

You are on the right path, but you need to do some work to present a more convincing narrative and stronger evidence sets to increase your chances.

All the best.

1 Like
7

hi Rafael -

Happy New Year! And thank you so much for such a detailed guidance!

Regarding OC3, I think my previous logic was a bit misleading since I was trying describe my work having directional and strategic impact. But if I can focus on more on the technical part, which is the key element to the success of these products that generated $$$ for the company, do you think this can fulfill the requirement of OC3? Another thing I actually have some concern is that due to internal policy, I might not be able to share too much internal content (e.g. code, etc). So other than reference letters that can validate my work and media report describing the product (although my name was not directly mentioned), do you think there is any other way to prove my importance?

Regarding OC4, I will concentrate all review experience together to strengthen this criteria. Although I didn’t speak at the conferences in person, those papers are published and can be seen online.

1 Like
8

@iam1cent Happy New Year

To fulfil OC3 - I strongly think you must demonstrate significant technical contribution in your role as a Senior Data Scientist, evidencing a high level of technical ownership, leadership, and measurable impact.

Individuals operating at this level typically take end-to-end ownership of data science systems and pipelines, from data ingestion and feature engineering through model development, validation, deployment, and monitoring. They are also responsible for both core modelling work (backend) and data products, analytics, or model outputs consumed by stakeholders. These are some of the things, I believe are expected to show technical contribution, going by your role. If for any reason you can’t show these because of company’s policies, then it is advisable you go for another criteria you can rightly provide the evidence its requires. Also remember your contribution has to be in a product-led digital technology company to rightly meet the criterion’s requirement.

And on OC4, concentrating all review experience together to strengthen this criteria is okay, but beyond this, you need to also add at least one more convincing evidence, as you need a minimum of two uniques evidence sets for each criterion.