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- Tasks: Lead a team to develop predictive models and enhance data workflows.
- Company: Join a fast-growing UK start-up specialising in predictive analytics for diverse industries.
- Benefits: Enjoy remote work flexibility and competitive salary up to £75,000.
- Why this job: Be part of an innovative team shaping data products that impact investors and hedge funds.
- Qualifications: 5+ years in data science, leadership experience, and expert Python skills required.
- Other info: Ideal for those passionate about data science and product innovation.
The predicted salary is between 54000 - 84000 £ per year.
This UK-based start-up has achieved rapid growth in just two years, now boasting a team of ~40 people across divisions. Following a successful funding round and with a strong pipeline ahead, they continue to scale at pace. They specialise in predictive analytics and KPI tracking across a broad range of companies and industries. Their predictive insights empower hedge funds and investors with critical performance data, ahead of public earnings reports.
As a Data Science Manager, you’ll take ownership of the end-to-end development of KPI prediction models and manage a team of data scientists, helping refine their workflows and ensure high-quality deliverables.
You will:
- Lead and mentor a team of data scientists in building predictive models.
- Oversee data cleaning, feature engineering, and model development pipelines.
- Build and maintain robust, scalable linear regression and statistical models for KPI forecasting.
- Drive improvements in internal tooling and API integrations.
- Collaborate closely with leadership, engineering, and the revenue team to translate business needs into data science solutions.
- Play a key role in product innovation, helping shape how new data products are designed and delivered.
What They’re Looking For:
- 5+ years’ experience in data science or a closely related field.
- Proven leadership experience — mentoring or managing junior data scientists.
- Expert Python programming skills (essential).
- Strong grasp of linear regression, statistical modeling, and data processing best practices.
- Proficient in SQL (MySQL preferred).
- Experience with web scraping, machine learning techniques, and dashboarding tools is a bonus.
- Familiarity with Docker, time series forecasting, or LLM technologies is advantageous.
- A background or exposure to finance is useful but not mandatory.
- Bachelor’s degree (or higher) in a quantitative or technical field.
- Strong coding samples (e.g., GitHub projects).
- Practical experience building production-level models and data pipelines.
- Ability to bridge data science and product development goals.
If this role looks it could be of interest, please reach out to Joseph Gregory, or apply here.
Locations
Data Science Manager employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Manager
✨Tip Number 1
Make sure to showcase your leadership skills during the interview process. Prepare examples of how you've successfully mentored or managed teams in the past, as this role requires strong leadership in guiding data scientists.
✨Tip Number 2
Brush up on your Python programming skills and be ready to discuss specific projects where you've implemented predictive models. Having concrete examples will demonstrate your expertise and problem-solving abilities.
✨Tip Number 3
Familiarise yourself with the company's focus on predictive analytics and KPI tracking. Understanding their business model and how data science plays a role in it will help you articulate how you can contribute to their goals.
✨Tip Number 4
Prepare to discuss your experience with SQL and any relevant tools like Docker or machine learning techniques. Being able to speak confidently about these technologies will set you apart from other candidates.
We think you need these skills to ace Data Science Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly any leadership roles you've held. Emphasise your skills in Python, SQL, and any experience with predictive modelling or KPI tracking.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and how your background aligns with the company's goals. Mention specific projects where you've led teams or developed predictive models to showcase your leadership and technical skills.
Showcase Your Coding Skills: Include links to your GitHub projects or any coding samples that demonstrate your expertise in Python and data science. Highlight any production-level models or data pipelines you've built to illustrate your practical experience.
Prepare for Technical Questions: Anticipate technical questions related to linear regression, statistical modelling, and data processing. Be ready to discuss your approach to building predictive models and how you manage team workflows to ensure high-quality deliverables.
How to prepare for a job interview at Harnham
✨Showcase Your Leadership Skills
As a Data Science Manager, you'll need to demonstrate your ability to lead and mentor a team. Prepare examples of how you've successfully guided junior data scientists in the past, focusing on specific challenges you helped them overcome.
✨Demonstrate Technical Proficiency
Make sure to highlight your expert Python programming skills and your experience with SQL. Be ready to discuss your coding samples and any relevant projects on platforms like GitHub that showcase your technical abilities.
✨Understand the Business Context
Familiarise yourself with the company's focus on predictive analytics and KPI tracking. Be prepared to discuss how your data science solutions can directly impact business outcomes, especially in relation to finance and investment.
✨Prepare for Technical Questions
Expect questions about linear regression, statistical modelling, and data processing best practices. Brush up on these topics and be ready to explain your thought process when building predictive models or handling data pipelines.
