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For Companies At a Glance
- Tasks: Create rapid prototypes and proofs of concept for innovative data solutions.
- Company: Join a forward-thinking tech company in the heart of London.
- Benefits: Competitive pay, flexible working, and opportunities for skill development.
- Other info: Fast-paced environment with great potential for career advancement.
- Why this job: Be at the forefront of data engineering and machine learning innovation.
- Qualifications: Experience in Python, SQL, and data science methodologies required.
The predicted salary is between 50000 - 65000 £ per year.
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
- Hands-on skills across: 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.
Prototyping Engineer (Data Engineering, Data Science and Machine Learning) employer: Whitehall Resources
Contact Detail:
Whitehall Resources Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Prototyping Engineer (Data Engineering, Data Science and Machine Learning)
✨Tip Number 1
Get your hands dirty with some quick projects! Build a few proofs of concept (PoCs) that showcase your skills in Python and data engineering. This not only demonstrates your ability to turn ideas into working solutions but also gives you something tangible to discuss during interviews.
✨Tip Number 2
Network like a pro! Reach out to professionals in the data engineering and machine learning space on platforms like LinkedIn. Share your PoCs and prototypes, and don’t hesitate to ask for feedback or advice. You never know who might have a lead on your next opportunity!
✨Tip Number 3
Practice your pitch! Be ready to explain your projects clearly and concisely. Focus on how you tackled ambiguous problems and iterated based on feedback. This will show potential employers that you can think independently and communicate effectively.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Make sure to highlight your hands-on experience with data pipelines and lightweight app development in your application. Let’s get you that Prototyping Engineer role!
We think you need these skills to ace Prototyping Engineer (Data Engineering, Data Science and Machine Learning)
Some tips for your application 🫡
Show Your Prototyping Skills: Make sure to highlight your experience in building proofs of concept and prototypes. We want to see how you can take loosely defined problems and turn them into working solutions quickly, so share specific examples from your past work!
Be Clear and Concise: When writing your application, keep it straightforward. We appreciate clarity, so avoid jargon and get straight to the point about your skills and experiences that relate to the role. Remember, we’re looking for practical outcomes!
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect the specific skills and experiences mentioned in the job description. We love seeing candidates who take the time to connect their background with what we’re looking for.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy and quick!
How to prepare for a job interview at Whitehall Resources
✨Know Your PoC Process
Make sure you understand the process of turning ideas into proofs of concept (PoCs) quickly. Be ready to discuss how you would approach a loosely defined problem and what steps you would take to validate an idea within days.
✨Showcase Your Technical Skills
Be prepared to demonstrate your hands-on experience with Python, SQL, and data engineering. Bring examples of past projects where you've built end-to-end prototypes, and be ready to explain your thought process and the tools you used.
✨Emphasise Independent Thinking
This role requires a high level of autonomy, so highlight your ability to work independently. Share experiences where you've taken initiative, iterated based on feedback, and communicated results effectively without much guidance.
✨Focus on Practical Outcomes
Remember, the goal is to assess feasibility quickly, not to write perfect code. Be ready to discuss how you've prioritised practical outcomes in your previous work and how you can apply that mindset to this role.