PRACYVA
Senior Data Scientist - AI
Manchester
Posted about 21 hours ago
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Role Purpose
Develop and evaluate machine learning, NLP and Generative AI solutions that address complex business problems. The role combines hands-on model development, BigQuery ML, experimentation, responsible AI documentation and mentoring within a collaborative Data and AI team.
Key Responsibilities
- Frame business challenges as measurable data science and machine learning problems, defining clear objectives, assumptions and success metrics.
- Develop, train, validate and improve classical machine learning and NLP models using Python and modern data science libraries.
- Build end-to-end modelling pipelines covering data preparation, feature engineering, training, validation, evaluation and production handover.
- Use BigQuery and BigQuery ML for scalable data processing, experimentation, model development and analytical workflows.
- Prototype Generative AI solutions using LLMs, embeddings and prompting, including basic Retrieval-Augmented Generation approaches.
- Design robust experiments and evaluation frameworks for both classical ML and LLM-based solutions.
- Assess model performance, quality, fairness, explainability and business outcomes using appropriate AI/ML metrics.
- Produce model cards, technical documentation and evidence required for governance and responsible AI review.
- Collaborate with ML Engineers, Data Engineers, Product teams and business stakeholders to operationalise solutions effectively.
- Mentor junior team members, review analytical work and contribute reusable standards and knowledge across the Data Science community.
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Required Experience
- 10+ years of experience across Data Science, Machine Learning Engineering or related applied AI roles.
- Proven experience translating ambiguous business needs into well-framed analytical or AI problems.
- Hands-on experience developing and evaluating classical machine learning, NLP and LLM-based solutions.
- Experience with experiment design, statistical evaluation, model metrics and clear communication of findings.
- Experience creating model documentation, model cards and governance-ready evidence.
Mandatory Skills
Capability
Required Experience
- Python
- Strong hands-on Python for data preparation, modelling, evaluation and reusable analytical code.
- Machine learning
- Model development, feature engineering, validation, optimisation and performance measurement.
- NLP
- Text preparation, classification, information extraction, embeddings and semantic retrieval pipelines.
- Generative AI
- Working knowledge of LLMs, embeddings, prompting and rapid prototype development.
- RAG Fundamentals
- Understanding of chunking, retrieval, grounding and basic evaluation of RAG-based solutions.
- BigQuery ML
- Practical experience using BigQuery and BigQuery ML for data processing, model training and evaluation.
- Experimentation
- Hypothesis-driven experiment design, AI/ML metrics and statistical interpretation.
- Model governance
- Model cards, explainability, responsible AI documentation and governance support.
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Preferred Experience
- Mentoring or coaching junior Data Scientists, analysts or ML practitioners.
- Exposure to agentic AI, autonomous workflows or LLM-based systems.
- Experience with Google Cloud Platform and Vertex AI.
- Production collaboration with ML Engineering, CI/CD and model monitoring teams.
- Experience delivering AI solutions within financial services or another regulated environment.
Education and Candidate Profile
- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, Statistics or a related discipline, or equivalent professional experience.
- Hands-on practitioner who combines analytical depth with pragmatic delivery and clear stakeholder communication.
- Curious and evidence-led, with a strong commitment to model quality, transparency and responsible AI.
- Collaborative team member who can mentor others while continuing to contribute directly to model development.
Technology Landscape
Python | SQL | BigQuery | BigQuery ML | Scikit-learn | XGBoost | NLP | LLMs | Embeddings | Prompting | RAG | Model Evaluation | Model Cards
What Success Looks Like
- Models and prototypes are grounded in clearly framed problems and measurable business outcomes.
- Classical ML and GenAI solutions are evaluated rigorously and documented for responsible adoption.
- BigQuery-based modelling workflows are scalable, reproducible and ready for engineering integration.
- Junior colleagues grow through practical mentoring, review and knowledge sharing.
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