Prototyping Engineer (Data Engineering, Data Science and Machine Learning)
Whitehall Resources are looking for a Prototyping Engineer (Data Engineering, Data Science and Machine Learning). This role is based onsite in London for an initial 6 month contract.
Inside IR35
The Role
We are looking for a highly autonomous contractor who can take ideas and concepts, think independently, and return within a few days with a working proof of concept.
This role is focused on rapid experimentation and validation, not long development cycles. The goal is to quickly assess whether ideas are viable and worth scaling.
- Your responsibilities:Build PoCs - Take loosely defined problems and turn them into proofs of concepts (PoCs) within days
- Combine data engineering, modelling and lightweight application development to test ideas end-to-end
- Convert PoCs to working Prototypes - Where a POC shows promise, there would be additional effort to grow it into a prototype (applying the concept to functional business needs) within 2-3 weeks
- Work independently with minimal guidance and iterate quickly based on feedback and communicate results clearly.
- What we are looking for:Strong ability to translate ideas into working solutions quickly
- Python (data processing, ML, prototyping).
- Data engineering (APIs, data pipelines, SQL, cloud data).
- Lightweight app development (APIs, simple frontends, notebooks, dashboards).
- Solid (not necessarily extensive)
knowledge on the statistical/mathematical fundamentals that support and proposed ML methodologies
.
- Experience building end-to-end prototypes, not just models.
- Comfortable working in ambiguous, fast-moving environments.
- Strong problem-solving and independent thinking.
- Nice to have:Experience integrating LLMs or AI services into applications
- Familiarity with modern data platforms (e.g. Snowflake)
- Experience with visualisation tools (e.g. Tableau, Plotly)
- Working knowledge of marketing and advertising
- What success looks like:You can go from idea → working PoC in 2–3 days
- You can go from working PoC to useful prototype in 2-3 weeks
- You unblock decisions by demonstrating feasibility quickly
- You focus on practical outcomes, not perfect code
Your Profile
- Essential skills/knowledge/experience:Strong hands‑on experience in Analytics & Reporting, with the ability to translate business requirements into measurable insights and KPIs.
- Advanced proficiency in SQL and Python for data extraction, transformation, analysis, and automation of analytical workflows.
- Solid foundation in Data Science and Machine Learning, including feature engineering, model development, evaluation, and performance monitoring.
- Practical experience with NLP techniques using scikit‑learn, applying text analytics to derive insights from unstructured data.
- Proven ability in API testing and automation, ensuring data quality, reliability, and stability of data/ML services.
- Excellent analytical and problem‑solving skills, with experience working closely with business stakeholders; exposure to Snowflake, Tableau, or Campaign Marketing analytics is an added advantage.
- Desirable skills/knowledge/experience:Strong experience in Advanced SQL
- Experience with API Testing automation
- Strong experience with Data Science
- Strong experience with Machine Learning, NLP Technologies with scikit-learn etc.
- Strong hands-on experience with Python (data processing, ML, prototyping)
- Strong hands-on experience with Data engineering (APIs, data pipelines, SQL, cloud data).
- Lightweight app development (APIs, simple frontends, notebooks, dashboards)
- Solid (not necessarily extensive)
knowledge on the statistical/mathematical fundamentals that support and proposed ML methodologies
.
- Experience with cloud data platforms (e.g., Snowflake) and modern data warehousing concepts for scalable analytics and ML workloads.
- Exposure to data visualization tools such as Tableau or similar BI platforms for creating executive‑level dashboards and self‑service reporting.
- Experience working in Agile delivery models and collaborating cross‑functionally with business, analytics, and engineering teams.
- Working knowledge of campaign marketing analytics, including customer segmentation, attribution, churn, and uplift analysis is beneficial.