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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 at the forefront of innovation, shaping new data products with a dynamic team.
- Qualifications: 5+ years in data science, strong Python skills, and leadership experience required.
- Other info: Ideal for those passionate about data and looking to make an impact in finance.
The predicted salary is between 60000 - 75000 £ 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.
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
Familiarise yourself with the company's recent projects and achievements. Understanding their predictive analytics focus will help you tailor your discussions during interviews, showcasing how your experience aligns with their goals.
✨Tip Number 2
Prepare to discuss your leadership style and experiences in mentoring data scientists. Highlight specific examples where you've successfully guided a team through complex projects, as this role heavily emphasises team management.
✨Tip Number 3
Brush up on your Python programming skills and be ready to demonstrate your expertise. Consider preparing a coding challenge or discussing past projects that involved building predictive models, as technical proficiency is crucial for this position.
✨Tip Number 4
Network with professionals in the data science field, especially those with experience in finance or predictive analytics. Engaging with industry peers can provide insights into the role and may even lead to referrals, increasing your chances of landing the job.
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, explain why you're passionate about data science and how your background aligns with the company's goals. Mention specific projects where you've led teams or developed predictive models.
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.
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.
How to prepare for a job interview at Harnham
✨Showcase Your Leadership Skills
As a Data Science Manager, you'll be expected to lead and mentor a team. Be prepared to discuss your previous leadership experiences, how you've supported junior data scientists, and any specific examples of successful projects you've overseen.
✨Demonstrate Technical Proficiency
Make sure to highlight your expert Python programming skills and your experience with linear regression and statistical modelling. Bring along coding samples or GitHub projects that showcase your abilities and practical experience in building production-level models.
✨Understand the Business Context
Familiarise yourself with the company's focus on predictive analytics and KPI tracking. Be ready to discuss how you can translate business needs into effective data science solutions, and think about how your work can impact their product innovation.
✨Prepare for Technical Questions
Expect to face technical questions related to data cleaning, feature engineering, and model development pipelines. Brush up on SQL and be ready to discuss any experience you have with web scraping, machine learning techniques, and dashboarding tools.
