Data Scientist

Senior Data Scientist: AI Training Data (2-4 Months Contract)
Company:  Bespoke Labs (VC-backed; founded by IIT & Ivy League alumni)

Location:

Remote
Role Type:  Contract (2-4 Months)
Time Commitment:  40 hrs/week (Full-time availability required)
Compensation:  Hyper-competitive hourly rate (matching top-tier Senior Data Scientist bands)

Experience:

6+ years
About Bespoke Labs  Bespoke Labs is a premier, VC-backed AI Research lab with an exceptionally talent-dense team of IIT and Ivy League alumni. We don’t just build tooling around AI—we build the massive-scale data systems and reasoning architectures that directly power next-generation models. Our research shapes the frontier of AI: we’ve published breakthroughs like GEPA, driven foundational datasets like Open Thoughts, and shipped state-of-the-art models including Bespoke-Mini Check and Bespoke-Mini Chart.

More on our website bespokelabs.ai :)

Role Overview  We are looking for a high-impact Senior Data Scientist for an intensive, 2-month sprint. You will leverage your deep expertise in production-grade machine learning and applied statistics to develop the algorithms and logic that curate and evaluate datasets for advanced AI model training.
This is not a traditional model-building or research role. We need a seasoned practitioner who has already owned the end-to-end DS lifecycle  will use your intuition for feature engineering, statistical validity, and large-scale data processing to programmatically generate, shape, and validate AI training data.

What You Will Do (The Contract)    Algorithm Design:  Design and implement custom statistical models and programmatic logic (e.g., anomaly detection, active learning, similarity scoring) to evaluate data quality, complexity, and redundancy ds-on At-Scale Coding:  Write scalable PySpark and Python (Num Py/Pandas) code to apply these algorithms across massive datasets, translating experimental logic into reliable, large-scale workflows.
Metric Formulation:  Develop custom quantitative metrics and heuristic benchmarks to rigorously assess the fidelity and suitability of data subsets for specific AI training objectives.
Validation & Iteration:  Run high-speed validation cycles, analyzing the output of data-curation algorithms to diagnose skew, bias, or noise, and iteratively refining the logic.
High-Level Curation:  Apply Senior-level domain expertise in predictive modeling and feature engineering to ensure the final training inputs meet the strict standards required for state-of-the-art ML systems.
What You Bring to the Table (Your Past Experience)  To be successful in this contract, you must have a track record of:
The End-to-End DS Lifecycle:  Framing problems, modeling, validation, production, and iteration.
Production Ownership:  Building and deploying ML and statistical models on large-scale datasets.
Large-Scale Data Processing:  Working with Apache Spark to develop scalable feature pipelines and offline training workflows.
Experimentation:  Designing and analyzing rigorous experiments (A/B tests, causal inference).
Impact:  Translating complex model outputs into clear product and business decisions.

Required Qualifications (Non-Negotiable)

Experience:

6+ years as a Data Scientist or Applied Scientist.
Production Background:  Proven ownership of models running in production environments.
Applied Statistics:  Strong background in applied statistics and experimentation frameworks.
Core Technical Skills

Languages:

Python (Num Py, Pandas, Scikit-learn, PyTorch / Tensor Flow) and Strong SQL.
Big Data:  Apache Spark (PySpark or Spark SQL) for large-scale data processing.
Methodologies:  Feature engineering, model evaluation, statistical modeling, and hypothesis testing.
Strong Signals (Highly Valued)    Scale:  Models trained on TB-scale datasets.
Domain Specificity:  Experience in high-complexity domains such as:
Recommendations, Pricing, Fraud / risk, Search / ranking, or Growth & experimentation.

Collaboration:

Experience deploying models alongside data engineering pipelines.
Out of Scope (Who Should Not Apply)   BI / reporting-only roles
SQL-only analysts
Research-only ML roles with no production ownership