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For Companies At a Glance
- Tasks: Lead hands-on data analysis and build traditional ML models for a major banking client.
- Company: Join a top-tier banking client focused on innovative data solutions.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact by solving analytical problems in the financial sector.
- Qualifications: 5+ years of Data Science experience with a focus on traditional machine learning.
- Other info: Collaborative environment with strong emphasis on communication and stakeholder engagement.
The predicted salary is between 36000 - 60000 £ per year.
Duration: 12 months
Location: Toronto (Hybrid)
Overview
Our Major Banking Client is seeking experienced Data Scientists to deliver hands-on, traditional machine learning solutions with a strong emphasis on exploratory data analysis (EDA), feature engineering, and classification modeling. This role is not focused on GenAI, LLMs, NLP, or ML platform engineering. Instead, it requires practitioners who have repeatedly built, validated, and productionized classical ML models in enterprise environments and can work closely with business stakeholders to solve structured analytical problems.
Key Responsibilities
- Lead hands-on exploratory data analysis, including data profiling, cleansing, transformation, and feature engineering.
- Translate business requirements into well-defined ML problem statements, with a clear focus on supervised learning use cases.
- Build, tune, and validate traditional ML models, with a strong emphasis on classification (regression and clustering as secondary).
- Select appropriate features, algorithms, and evaluation metrics aligned to the business objective.
- Perform model testing, cross-validation, and performance analysis; clearly articulate trade-offs and limitations.
- Produce clear and complete model documentation covering methodology, assumptions, and results.
- Collaborate with Data Engineers on data readiness and ETL (not a data engineering or platform role).
- Partner with MLOps teams to support model deployment and monitoring (not an ML engineering role).
- Communicate analytical insights and model outcomes effectively to business stakeholders.
Must Have Skills & Experience
- 5+ years of hands-on Data Science experience focused on traditional machine learning.
- Demonstrated experience building classification models end-to-end (strongly preferred).
- Deep practical experience with EDA and feature engineering using Python (Pandas, NumPy, SciPy).
- Strong experience with classical ML algorithms (e.g., logistic regression, decision trees, random forests, gradient boosting, k-means).
- Solid proficiency in SQL for data extraction and analysis.
- Experience validating and testing models using appropriate statistical and ML metrics.
- Proven ability to document models and analytical approaches for enterprise use.
- Strong communication skills with experience engaging business stakeholders directly.
Nice to Have
- Experience within the financial services or banking industry.
- Exposure to cloud platforms such as AWS, Azure, or GCP.
- Familiarity with ML lifecycle tools and frameworks.
- Experience working in Agile project environments.
- Knowledge of advanced analytics, A/B testing, or optimization techniques.
Data Scientist – Traditional Machine Learning (Classification-Focused) for our Tier1 Banking client in London employer: S.i. Systems
Contact Detail:
S.i. Systems Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist – Traditional Machine Learning (Classification-Focused) for our Tier1 Banking client in London
✨Tip Number 1
Network like a pro! Reach out to connections in the banking and data science fields. Attend meetups, webinars, or industry events to get your name out there. You never know who might have a lead on that perfect Data Scientist role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your classification models and EDA projects. Use platforms like GitHub to share your code and results. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with traditional ML algorithms and how you've tackled real-world problems. Practise explaining complex concepts in simple terms – it’ll impress those business stakeholders!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities tailored for Data Scientists like you. Plus, it’s a great way to ensure your application gets seen by the right people. Let’s land that job together!
We think you need these skills to ace Data Scientist – Traditional Machine Learning (Classification-Focused) for our Tier1 Banking client in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with traditional machine learning, especially classification models. We want to see how you've tackled similar challenges in the past, so don’t hold back on those details!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for this role. Mention your hands-on experience with EDA and feature engineering, and how you can translate business needs into ML solutions.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python, SQL, and classical ML algorithms. We’re looking for someone who can hit the ground running, so make sure these skills are front and centre in your application.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at S.i. Systems
✨Know Your ML Models Inside Out
Make sure you can discuss your experience with traditional machine learning models, especially classification techniques. Be ready to explain how you've built, validated, and deployed these models in past roles, as this will show your hands-on expertise.
✨Master Exploratory Data Analysis (EDA)
Brush up on your EDA skills! Be prepared to talk about how you've performed data profiling, cleansing, and feature engineering. Use specific examples from your previous work to illustrate your process and the impact it had on your projects.
✨Communicate Like a Pro
Since you'll be working closely with business stakeholders, practice articulating complex analytical insights in simple terms. Think of ways to present your findings clearly and effectively, as this will demonstrate your ability to bridge the gap between technical and non-technical audiences.
✨Prepare for Technical Questions
Expect to face technical questions related to SQL, model testing, and performance analysis. Review key concepts and be ready to discuss how you select features, algorithms, and evaluation metrics based on business objectives. This will showcase your analytical thinking and problem-solving skills.
