Data Science Architect

Pfizer Inc.; Swindon; Hybrid; GBP 90,000 - 107,000; Full time

Job summary

A global healthcare company seeks a Data Engineering Lead to create complex data science problems for AI projects. This role, based in the UK, involves designing and documenting computational problems that reflect real-world business challenges across various sectors. Candidates must possess an advanced degree in a quantitative field and have over five years of impactful data science experience. This position offers a competitive salary and a hybrid work model, working partially from the Bristol office.

Qualifications

• 5+ years of hands-on data science experience with proven business impact.
• Portfolio showcasing real-world problem-solving.
• Expert programming in Python and strong understanding of AI/ML applications.

Responsibilities

• Design computational data science problems for advanced AI technology.
• Create problems using Python programming for data science.
• Document problem statements with realistic business contexts.

Skills

Python programming

Statistical analysis

Machine learning

SQL

Data visualization

Education

Master’s or PhD in Data Science, Statistics, Mathematics, or Computer Science

Tools

pandas

numpy

scikit-learn

TensorFlow

PyTorch

Job description

Job Title

Data Engineering Lead

Overview

At Mindrift, innovation meets opportunity. We believe in using the power of collective intelligence to ethically shape the future of AI. The Mindrift platform connects specialists with AI projects from major tech innovators. Our mission is to unlock the potential of Generative AI by tapping into real‑world expertise from across the globe.

Position Overview

We are seeking experienced data scientists to create computationally intensive data science problems for an advanced AI evaluation project. This is a remote, project‑based opportunity for experts who can design challenging problems that require computational methods to solve and mirror the full data science lifecycle—from data acquisition and processing to statistical analysis and actionable business insights.

Responsibilities

• Design original computational data science problems that simulate real-world analytical workflows across industries (telecom, finance, government, e-commerce, healthcare).
• Create problems requiring Python programming to solve (using pandas, numpy, scipy, sklearn, statsmodels, matplotlib, seaborn).
• Ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks).
• Develop problems requiring non‑trivial reasoning chains in data processing, statistical analysis, feature engineering, predictive modelling, and insight extraction.
• Create deterministic problems with reproducible answers—avoid stochastic elements or require fixed random seeds for exact reproducibility.
• Base problems on real business challenges: customer analytics, risk assessment, fraud detection, forecasting, optimisation, and operational efficiency.
• Design end‑to‑end problems spanning the complete data science pipeline (data ingestion, cleaning, EDA, modelling, validation, deployment considerations).
• Incorporate big‑data processing scenarios requiring scalable computational approaches.
• Verify solutions using Python with standard data science libraries and statistical methods.
• Document problem statements clearly with realistic business contexts and provide verified correct answers.

Problem Domains (Examples)

• Business Analytics & Predictive Modeling – customer segmentation, churn prediction, CLV modelling; fraud detection with precision‑recall optimisation; A/B‑test calculations and experimental design; cohort analysis and retention rate computations; model‑evaluation metrics and performance optimisation.
• Statistical Analysis & Data Processing – hypothesis testing with multiple‑comparison corrections; causal inference (propensity‑score matching, difference‑in‑differences); complex ETL pipeline logic and multi‑source data integration; missing‑data imputation and outlier‑detection algorithms; feature engineering (encoding, interactions, dimensionality reduction).
• Time Series & Forecasting – trend decomposition and seasonality detection; stationarity testing and autocorrelation calculations; forecast‑accuracy metrics and moving‑average, exponential‑smoothing etc.
• Optimization & Operations Research – resource‑allocation and constraint‑satisfaction problems; cost‑benefit analysis and capacity‑planning; network analysis and supply‑chain calculations; optimisation algorithms and convergence analysis.
• Database & Mathematical Foundations – complex SQL query optimisation and window functions; probability computations for business scenarios; linear algebra operations and matrix factorisation; information‑theory metrics (entropy, mutual information).

Requirements

• Education: Advanced degree (Master’s or PhD) in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
• Experience: 5+ years of hands‑on data science experience with proven business impact; portfolio of completed projects and publications showcasing real‑world problem‑solving.
• Technical Skills: Expert Python programming for data science (pandas, numpy, scipy, scikit‑learn, statsmodels); statistical analysis and machine learning – deep understanding of algorithms, methods, and their practical applications; SQL and database operations for data manipulation and analysis; modern AI/ML – highly valued (GenAI technologies, LLMs, RAG, prompt engineering, vector databases); MLOps practices and model deployment workflows; knowledge of modern frameworks (TensorFlow, PyTorch, LangChain).
• Professional Skills: Ability to design problems reflecting real‑world business scenarios and industry challenges; strong analytical and problem‑solving skills with attention to detail; experience translating business requirements into technical solutions; clear technical writing and documentation skills; proficiency in English.

Preferred Qualifications

• Cross‑industry experience (finance, telecommunications, healthcare, technology).
• Research background with publications or case studies.
• Teaching, mentoring, or training experience.
• Professional certifications in data science or machine learning.

Compensation

Salary: £90,440 – £106,400 per annum. Task‑based compensation. Full‑time position with a hybrid working style – at least two days per week (≈40 % of time) at our Bristol office.

Application Process

• Submit CV/Resume highlighting ML research or engineering background.
• Domain knowledge test.
• Python test.
• Interview.