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π§ Data Scientist: Machine Learning & Python Specialist π
Python Developer
Location: London
Work mode: Onsite
Experience: 2β5 years
UK based candidates only - CITIZEN & DEPENDENTS
We are seeking an innovative and results-driven Data Scientist with a strong focus on Machine Learning and deep proficiency in Python. You will be instrumental in transforming complex data into actionable insights, building predictive models, and driving business strategy using cutting-edge analytical techniques. This role is for a hands-on individual who is excited to move models from the lab into production.
Key Responsibilities
- Model Development & Implementation: Design, develop, train, validate, and deploy advanced Machine Learning models (e.g., classification, regression, clustering, deep learning) to solve complex business problems.
- Data Wrangling & Analysis: Perform comprehensive Exploratory Data Analysis (EDA), data cleaning, feature engineering, and transformation on large, complex, and sometimes unstructured datasets.
- Coding & Automation: Write production-quality, highly efficient, and scalable code primarily in Python for data processing, analysis, and model creation.
- Experimentation & Optimization: Conduct A/B testing, hypothesis testing, and rigorous model validation, continually iterating and tuning algorithms to maximize performance, accuracy, and efficiency.
- Collaboration & Communication: Work closely with cross-functional teams (e.g., product managers, engineers, business stakeholders) to define project scope, interpret model results, and clearly present data-driven recommendations to both technical and non-technical audiences.
- Deployment & MLOps: Collaborate with ML/Data Engineers to deploy, monitor, and maintain ML models in a production environment, ensuring stability and performance over time.
Reasons to use Rodeo
Iβm in my final year doing Economics and I donβt know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer β it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) donβt need a masters. Letβs look at the ones youβd be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet β that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant β 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
Youβve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon β deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme β client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right β and where to focus when applying.
Required Skills and Qualifications
Technical Expertise (The Core)
- Programming: Expert proficiency in Python and its core data science libraries: Pandas, NumPy, and SciPy.
- Machine Learning: Deep, practical experience with popular ML frameworks and libraries: scikit-learn, TensorFlow, or PyTorch.
- Statistics & Math: Strong foundation in statistical modeling, probability, hypothesis testing, regression analysis, and multivariate calculus/linear algebra for understanding model mechanics.
- Databases & Querying: Proficiency in SQL for extracting, manipulating, and preparing data from relational databases. Experience with NoSQL databases is a plus.
- Big Data/Cloud: Experience with big data tools (Spark, Hadoop) and cloud computing platforms (AWS, Azure, or GCP) for scalable ML workflows.
- Visualization: Ability to create clear, compelling data visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to communicate insights.


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Education & Experience
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
- [Number] years of professional experience as a Data Scientist or in a highly quantitative role.
Soft Skills (Your X-Factor)
- Curiosity: A strong passion for data and an inherent curiosity to explore, question, and challenge assumptions.
- Problem-Solving: Excellent analytical and structured thinking skills to tackle complex, ambiguous business problems.
- Storytelling: The ability to translate complex statistical and ML outputs into simple, business-relevant narratives and recommendations.
βIt took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what Iβve been looking for.β
Jessica, London
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