---
title: 'Senior Applied Data Scientist | NDA at GT'
canonical: 'https://feeny.ai/job/senior-applied-data-scientist-nda-gt-warsaw-m6mxj01qh8nq'
type: 'job'
last_seen: '2026-09-10'
---

# Senior Applied Data Scientist | NDA at GT

- **Company:** GT
- **Location:** Warsaw, Poland
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-31
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/gt-hq/0f4084fe-b775-44a7-b3b2-9eb3c80d1ab2

## Job description

GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands. On behalf of our client, GT is looking for a Senior Applied Data Scientist interested in developing and testing new ML, embedding, and LLM-based approaches to solve complex data matching problems at scale.

## About the Client

Our client is a leading global management consultancy known for tackling some of the world’s most complex business challenges. With a focus on strategy, transformation, and performance improvement, the firm partners with major organizations across industries to drive lasting impact.

## About the Role

We are looking for a Senior Applied Data Scientist to improve how entity resolution is performed at scale. You will develop and test new ML, embedding, and LLM-based approaches for matching complex business records across multiple data sources. The work is centered on model quality, experimentation, and evaluation; engineering partners will help productionize successful approaches. A key part of the role is exploring how newer foundation-model techniques can improve matching quality while remaining practical and scalable for very large datasets.

Responsibilities: Develop better ways to match company records

- Build new ML, embedding, and LLM-based approaches for matching entities
- Improve how the system handles messy data, including name variations, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies.
- Develop scoring and ranking approaches to distinguish accurate matches from duplicates, similar-looking records, and unrelated entities.
- Evaluate and implement AI and machine learning techniques to improve matching quality while considering accuracy, scalability, and cost.
- Design approaches that can operate efficiently at scale, taking model usage and computational cost into consideration.

Improve evaluation, experimentation, and match quality

- Define and improve methods for evaluating match quality, including precision, recall, false positives, false negatives, confidence, coverage, and manual review effort.
- Assist in building trusted benchmark sets that allow us to compare new models against the current matching engine before production rollout.
- Explore LLM-assisted review and validation to assess matching performance and benchmark more scalable approaches.
- Turn ambiguous matching problems into clear hypotheses, experiments, metrics, and recommendations.

Partner with engineering to bring successful ideas into production

- Work closely with data engineering and software engineering teams to turn promising prototypes into production-ready matching logic.
- Provide engineering partners with clear model specifications, evaluation results, expected behavior, edge cases, and rollout requirements.
- Help determine the most appropriate matching techniques based on data characteristics, confidence levels, and cost considerations.
- Continuously evaluate matching performance, investigate regressions, and recommend improvements to models and matching logic.
- Clearly communicate technical tradeoffs related to matching performance, scalability, cost, latency, explainability, and operational considerations.

Essential knowledge, skills & experience:

- 5–8 years of relevant experience in Data Science, Applied Data Science, Applied Machine Learning, or a similar role.
- Strong applied ML fundamentals, with hands-on experience building and evaluating models on real data.
- Excellent Python and SQL skills.
- Practical experience with embeddings, semantic similarity, LLMs, or related AI techniques.
- Hands-on experience training supervised and unsupervised models, including classification and NLP tasks.
- Working knowledge of neural network and transformer architectures.
- Proficiency with common ML frameworks such as TensorFlow, PyTorch, and PyCaret.
- Experience retraining a taxonomy classifier or maintaining classification models in production.
- Experimental judgment: able to define baselines, metrics, test sets, and error analysis that show whether quality improved.
- Ability to explain model behavior, tradeoffs, and edge cases clearly to engineering and business partners.

Nice-to-have:

- Experience with entity resolution, record linkage, deduplication, or similar matching problems.
- Experience with ranking, similarity scoring, retrieval, clustering, or candidate generation.
- Experience applying LLMs or embeddings to business problems where cost and scale matter.
- Exposure to large-scale data platforms such as Spark, Snowflake, Databricks, or BigQuery.
- Familiarity with company, domain, website, firmographic, or other business-entity data.

Interview Steps:

- GT interview with Recruiter
- Technical interview
- Final interview

## About GT

## Company Overview
- **One-liner**: GT is a boutique tech partner that helps high-growth companies design, build, and scale digital products, offering end-to-end product development, AI/ML implementation, and team scaling services.
- **Entity Type**: Private (Bootstrapped)
- **Headquarters**: Multiple locations across Europe, North America, and the UK (remote-first)
- **Founded**: 2019
- **Founders**: Andy Baynes (Co-founder & Chairman), Nikita Gorskykh (Co-founder & CEO), John Harris (Co-founder & CTO)

## Core Business
- **Primary Industry**: Technology Consulting & Custom Software Development
- **Target Customers**: High-growth companies, from fast-moving startups to global enterprises, primarily in the UK, US, Canada, and Europe
- **Mission**: To become a meeting point for the world’s best engineers and product companies with a tremendous and meaningful mission to implement in life [dou.eu](https://dou.eu/en/companies/https-www-gt-hq-com-career)

## Products & Services
- **Activate (Exploratory Projects)**: Focused, short-term engagements for AI adoption, migration assessment, or guiding a product’s next direction.
- **Scale (Team Expansion)**: Mid-to-long-term team scaling — a handpicked team of engineers hired specifically to client requirements, onboarded within 6 weeks, with no rotation between projects.
- **Deliver (End-to-End Development)**: Full management of a project from brief to launch, handling complete product development and seamless delivery.
- **Implement AI**: From exploring AI opportunities to fully implementing tailored AI/ML and data science solutions that drive growth.
- **Expertise Areas**: Full-stack development, AI/ML and data science, product management, data insights & visualization, quality management, and security advisory.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private, bootstrapped company)
- **Key Metric**: 686 total employees (as of mid-2026) [builtin.com](https://builtin.com/company/gt)
- **Notable Clients/Projects**: McLaren, Universal Studios, Jaguar Land Rover, Rio ESG, Eagle Genomics, Lumin Fitness [dou.eu](https://dou.eu/en/companies/https-www-gt-hq-com-career)
- **Growth Signals**: Founded in 2019 by leaders from Apple, Nest, Google, and GSK; operates across 26+ locations globally; strong remote-first culture with 91% eNPS and a Glassdoor rating of 4.6 [gt-hq.com](https://gt-hq.com/career)

## Competitive Advantages
- **Founding Team Pedigree**: Built by former executives from Apple, Nest, Google, and GSK — deep expertise in product development at scale.
- **No Engineer Rotation**: Unlike many outsourcing firms, GT never rotates engineers between projects, ensuring continuity and deep client relationships.
- **Boutique, High-Touch Model**: Focused on quality over volume, working with ambitious, world-changing products.
- **Data-Driven & Engineer-First Culture**: Emphasis on data-informed decisions, feedback loops, and resourceful thinking.

## Strategic Focus
- **Geographic Expansion**: Growing teams across Europe, North America, and the UK with remote and hybrid opportunities.
- **AI/ML Leadership**: Heavy investment in AI and data science capabilities to help clients adopt emerging technologies.
- **Client Diversity**: Serving both fast-moving startups and large-scale data transformation initiatives across industries like AR/VR, automotive, medtech/healthcare, and data science.

## Why Work Here
- **Culture**: Described as "engineer first and care driven" — a team of curious minds, creative thinkers, and builders who care about impact. Values include drive innovation, take responsibility, think resourcefully, use feedback and data, and exceed expectations.
- **Work Model**: Remote-first with hybrid options; employees can choose their own pace and working style — impact is what matters.
- **Perks**: Health insurance, psychotherapy coverage, learning budget, self-development investment, sports and mindfulness practices, mental support allowance, and fun team parties.
- **eNPS**: 91% (internal survey) [gt-hq.com](https://gt-hq.com/career)
- **Glassdoor**: 4.6 rating
- **Team Environment**: Described as initiative, hard-working, mature, curious, and optimistic — a "glass-half-full" community.

## Sources
1. [gt-hq.com](https://gt-hq.com/career)
2. [gt-hq.com](https://gt-hq.com/)
3. [builtin.com](https://builtin.com/company/gt)
4. [dou.eu](https://dou.eu/en/companies/https-www-gt-hq-com-career)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/gt-hq)

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