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Data Scientist - Extensions
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Data Scientist - Extensions
Data Scientist - Extensions (Remote / United Kingdom-Based)
Operating alongside [Partner Company], who will manage all applications and next steps.
About the Role
As a Data Scientist - Extensions, you will drive the advancement of cutting-edge AI systems designed for enterprise decision-making at scale. Your work will focus on structured data challenges, improving predictive performance across large-scale tabular models for diverse industries and high-stakes applications.
This role straddles research and production, blending technical depth with impactful delivery. You will collaborate with interdisciplinary teams—research, engineering, and applied AI—to dissect model behaviour, refine algorithms, and push capabilities forward. The environment thrives on innovation, fast-paced execution, and foundational AI contributions. Your research directly shapes how enterprises harness data for faster, accurate, and mission-critical decisions.
Key Responsibilities
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Research & Methodology:
- Develop advanced data science methods to enhance predictive performance across enterprise-level datasets and diverse prediction tasks.
- Design and execute rigorous empirical experiments, build robust benchmarks, and validate findings using statistically sound methods.
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Engineering & Productionisation:
- Craft, implement, and maintain production-grade Python components, prioritising scalability, reusability, and correctness.
- Adapt traditional ML models (e.g., XGBoost, LightGBM, CatBoost) and emerging tabular foundation models (e.g., TabPFN, CARTE).
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Real-World Optimisation:
- Analyze enterprise data challenges: missing values, class imbalance, feature cardinality, and distribution shifts.
- Propose solution architectures that ensure robustness across domains like finance, healthcare, supply chain, retail, and industrial applications.
- Recommend tools like DuckDB, Polars, or benchmark them against in-process analytics engines.
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Collaboration & Impact:
- Establish cross-functional partnerships with research, engineering, and applied AI teams to bridge gaps between theory and real-world outcomes.
- Apply ML research findings to customer-facing projects, translating quality metrics into tangible product improvements.
- Document workflows, best practices, and technical standards to foster reproducibility and team knowledge.
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- Problem Solving: Launch end-to-end projects from research hypothesis to scalable production deployment. Approach challenges with an analytical mindset, weighted toward measurable impact and empirical validation.
Requirements
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Experience:
- 5+ years in data science, machine learning, or applied ML engineering.
- Hands-on experience joining official comp ranges in Tableparing (highly desirable):
- Emerging projects on Kaggle, DrivenData or contributing to leading ML libraries.
- Evidence of independent R&D work: publishing AI/ML research papers or securing patents within industry research.
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Technical Expertise:
- Proficient in Python, with rigorous experience in pandas, NumPy, scikit-learn, and Numba/JAX for production at scale.
- Mastery of gradient boosting frameworks—XGBoost, LightGBM, CatBoost—including transformations, optimisations, and pitfalls.
- Strong applied knowledge of:
- Handling real-world tabular data: imputation, class imbalance (SMOTE, Upsampling, Focal Loss), and validation/concept drifts.
- ML model principles across classification, regression, ranking, and time series.
- Familiarity with tabular ML standards and foundation models (e.g., TabPFN, CARTE).
- Experience with ** modern in-process/efficient query engines** (Polars, DuckDB) or equivalent for tabular pipelines.
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Capability Portrait:
- Proven track record describing research ideas (e.g., theoretical bills/ technical narratives) and prototyping/packaging them as permissive, concise, production code (ie, instrumentability proof-of-concepts).
- Analytic curiosity: Ability to experiment iteratively (e.g., design multiple models and systematically establishing benchmarks).
- Hands-on storytelling skills: Using Python (Matplotlib, Seaborn, Plotly) to explain performance trade-offs to non-technical stakeholders.
- Advocacy for openness and documentation conventionality, including code comments, config files, and relative scalability reports.
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Optional Advantages:
- Candidates with hands-on experience in multi-disciplinary applied AI teams (combining product managers, engineers, and data scientists) will find greater value.
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Benefits & Culture
A mission-driven workplace with high autonomy, low bureaucracy, and a growth-first mindset:
- Competitive package: Base salary + equity (commitment to both competitive salary and transformative-% model).
- Work-Life Harmony:
- Comprehensive health insurance (UK, Canada and US plans).
- Generous parental leave (including adoptive and surrogate mothers).
- Resource Support:
- MBA/Structured application programs + access to data science/deep learning frameworks.
- Full-budget relocation allowance (for office-based candidates).
- Technical Challenges & Growth:
- Opportunity to shape foundational AI systems under guised research trends — capacidad to transition ideas between stages of execution via machine learning.
- Culturally low ego, metrics-driven, with emphasis on collaboration and measurable impact.
- Open access to cutting-edge databases, residential v. rustic deep learning libraries, and DIY academic technologies.
How Jobgether Works
Applications are processed via an independent AI-assisted matching system before being reviewed by the partner company.
- Your application is automatically evaluated against the role’s core requirements.
- Top-matching candidates are forwarded directly to the client’s internal hiring team for next steps (interviews, technical assessments).
- Final hiring decisions and all subsequent communications are managed exclusively by the employer.
Why Apply Through Jobgether?
By proceeding, you affirm your consent to Jobgether processing your data per GDPR/CCPA. Data handling is restricted to:
- Core recruitment:
- Objective candidate matching via computational analysis.
- Enhancing application accessibility to·our·partner.
- Candidate rights:
- Access, correction, deletion, and opting out of automated processing.
- AI role transparency:
- Tools may screen resumes, assess skill alignment, and flag potential inconsistencies.
- AI outputs supplement—not replace—human review.
For more details, contact Jobgether’s cluster Model engineers at [contact method]. This hiring policy prioritises efficiency and fairness.
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