Senior Data Scientist / Machine Learning Engineer
Senior Data Scientist / Machine Learning Engineer
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Senior Consultant – AI, Machine Learning and Data Science
Contract Senior Data Scientist / ML Engineer£600-800/day | Outside IR35 | Hybrid (Central London, 2 days/week)
We’re working with a specialist consultancy delivering high-impact machine learning solutions to private equity-backed businesses. They are looking for an experienced Data Scientist or ML Engineer to support a live project, applying classical machine learning to solve tangible, high-value problems.
You will be joining a small, collaborative team of engineers and data scientists on-site 2 days per week in Central London.
The work focuses on traditional ML use cases, such as:
Optimisation modelling to improve manufacturing throughput
Predictive modelling to anticipate and reduce asset downtime
Customer churn prediction and mitigation
Next-best-action modelling for sales agents
Geospatial modelling to inform store and asset placement decisions
Must-Have Requirements:
3-5+ years’ experience applying classical ML in commercial settings
Excellent Python coding skills (production-grade, using libraries like Pandas, NumPy, scikit-learn)
Strong understanding of supervised and unsupervised learning methods (regression, classification, clustering, tree-based models, etc.)
Comfortable working across the full ML lifecycle
Previous exposure to ambiguous or evolving problem spaces, ideally within consulting or client-facing environments
- Experience with AWS / Azure and SageMaker
Clear and confident communicator, able to contribute to client conversations and work collaboratively with delivery teams
Degree from a top university in a quantitative discipline (Master’s preferred)
Based in London and able to attend the client site 2 x per week.
Nice-to-Haves:
Experience with geospatial modelling, time series forecasting, or operational optimisation
DBT
Interviews are taking place this week. Start ASAP.
Please email
Contract Senior Data Scientist / ML Engineer£600-800/day | Outside IR35 | Hybrid (Central London, 2 days/week)
We’re working with a specialist consultancy delivering high-impact machine learning solutions to private equity-backed businesses. They are looking for an experienced Data Scientist or ML Engineer to support a live project, applying classical machine learning to solve tangible, high-value problems.
You will be joining a small, collaborative team of engineers and data scientists on-site 2 days per week in Central London.
The work focuses on traditional ML use cases, such as:
Optimisation modelling to improve manufacturing throughput
Predictive modelling to anticipate and reduce asset downtime
Customer churn prediction and mitigation
Next-best-action modelling for sales agents
Geospatial modelling to inform store and asset placement decisions
Must-Have Requirements:
3-5+ years’ experience applying classical ML in commercial settings
Excellent Python coding skills (production-grade, using libraries like Pandas, NumPy, scikit-learn)
Strong understanding of supervised and unsupervised learning methods (regression, classification, clustering, tree-based models, etc.)
Comfortable working across the full ML lifecycle
Previous exposure to ambiguous or evolving problem spaces, ideally within consulting or client-facing environments
- Experience with AWS / Azure and SageMaker
Clear and confident communicator, able to contribute to client conversations and work collaboratively with delivery teams
Degree from a top university in a quantitative discipline (Master’s preferred)
Based in London and able to attend the client site 2 x per week.
Nice-to-Haves:
Experience with geospatial modelling, time series forecasting, or operational optimisation
DBT
Interviews are taking place this week. Start ASAP.
Please email
Desired Skills and Experience
Predictive Modelling, Python Machine Learning, Full ML Lifecycle
Seniority level
Seniority level
Mid-Senior level
Employment type
Employment type
Contract
Job function
Job function
Information Technology
Industries
Technology, Information and Internet
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