Senior data scientist

Allscreens Nationwide Ltd; Manchester; On-site; GBP 92,000 - 129,000; Part time

Job summary

A Senior Data Scientist (Contract) at Allscreens Nationwide Ltd in Manchester City Centre will own end‑to‑end data science work from problem framing, through modelling, to production deployment and monitoring. You will work with a small team of data scientists and engineers, delivering high‑impact projects, mentoring juniors, and communicating results to non‑technical stakeholders.

Strong Python, statistics, and production experience are essential; familiarity with recommender systems and GenAI

Qualifications

• Proven track record shipping models into production.
• Strong Python and statistics foundation for experimental design.
• Experience with recommender systems or personalization desirable.
• Exposure to LLMs or generative AI is a plus.

Responsibilities

• Model ownership from design to production deployment and monitoring.
• Apply statistical modelling and ML techniques to real business problems.
• Design and run experiments (A/B tests, causal inference) to validate impact.
• Collaborate with Product, Engineering and MLOps to scale prototypes.
• Mentor junior data scientists on technical approaches and best practices.
• Translate complex findings into clear business insights.

Skills

Python

Statistics

Experimentation

Production ML

NLP/LLM

Communication

Recommender systems

Tools

NumPy/Pandas/Scikit-learn

PyTorch

TensorFlow

SQL

MLflow

Azure ML

SageMaker

Databricks

Job description

Senior Data Scientist - Contract Role, Manchester City Centre

Role Title: Senior Data Scientist (Contract)Contract Type: Inside IR35, initial 6-month term, strong likelihood of extension

About the Business

We're partnering with a fast scaling technology business in the North of England that's investing heavily in data driven product development. This is a hands on, individual contributor focused role for a Senior Data Scientist who wants to own technical delivery on high impact projects, without the overhead of formal line management.

About the Role

This role is for someone who thrives on solving ambiguous, high value problems independently, taking a business question, shaping it into a data science approach, and owning it from start to finish through to production. You'll work closely with a small team of data scientists and engineers, contributing technical depth and mentoring more junior colleagues along the way, but the core of the role is hands on delivery.

Key Responsibilities

• Model ownership from start to finish: Design, build, and deploy production grade machine learning models, from problem framing and experimentation through to live deployment and monitoring.
• Technical depth: Apply strong statistical modelling, ML, and (where relevant) NLP/LLM techniques to solve real business problems, not just exploratory analysis.
• Experimentation and measurement: Design and run experiments (A/B testing, causal inference) to validate impact and guide decision making.
• Cross-functional delivery: Partner closely with Product, Engineering, and MLOps teams to move ideas from prototype into scalable production systems.
• Mentorship: Support and coach junior data scientists on technical approach, code quality, and best practice, without formal management responsibility.
• Stakeholder communication: Translate complex technical findings into clear, actionable insights for non-technical stakeholders.
• Best practice: Contribute to how the wider data science function approaches tooling, methodology, and model governance.

Technical Environment

• Languages/Tools: Python (NumPy, Pandas, Scikit learn), deep learning frameworks (PyTorch/TensorFlow), SQL.
• MLOps: MLflow, cloud ML platforms (Azure ML, AWS SageMaker, or Databricks).
• Focus areas: Predictive modelling, recommender systems/personalisation, and increasingly, applied LLM/GenAI use cases, reflecting where the market is heading right now.

What We're Looking For

• Strong commercial experience as a data scientist, with a proven track record of shipping models into production, not just notebook-based analysis.
• Expert level Python and solid grounding in statistics/experimental design.
• Experience with recommender systems, personalisation, or ranking techniques is highly desirable.
• Exposure to LLMs, Generative AI, or conversational AI products is a strong plus given current market demand.
• Comfortable taking an unclear business problem and shaping it into a scoped data science approach, including recognising where ML isn't the right answer.
• Strong communication skills, able to flex between technical depth and clear business-facing summaries.