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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 innovative product development and make a real impact in data science.
- Qualifications: 5+ years in data science, leadership experience, and expert Python skills required.
- Other info: Ideal for those passionate about bridging data science with 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.
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
Showcase your leadership skills by discussing any previous experience where you mentored or managed a team. Highlight specific examples of how you helped junior data scientists grow and succeed in their roles.
✨Tip Number 2
Familiarise yourself with the company's focus on predictive analytics and KPI tracking. Be prepared to discuss how your experience aligns with their goals, particularly in building robust models and improving internal tooling.
✨Tip Number 3
Prepare to demonstrate your technical skills, especially in Python and SQL. Consider bringing along coding samples or projects from your GitHub that showcase your ability to build production-level models and data pipelines.
✨Tip Number 4
Research the latest trends in data science, particularly in finance and predictive modelling. Being able to discuss current methodologies or tools like Docker and machine learning techniques will show your commitment to staying updated in the field.
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 to showcase your expertise.
Showcase Your Coding Skills: Include links to your GitHub projects or any coding samples that demonstrate your proficiency in Python and your ability to build production-level models. This will give the hiring team insight into your technical capabilities.
Highlight Relevant Experience: If you have experience in finance or have worked on projects involving predictive analytics, make sure to highlight this in your application. It shows that you understand the industry and can contribute effectively from day one.
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
Be ready to discuss your expertise in Python and SQL. Bring along coding samples or projects from GitHub that highlight your skills in building predictive models and data pipelines, as this will show your hands-on experience.
✨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 Collaborative Scenarios
Since collaboration is key in this role, think of examples where you've worked closely with cross-functional teams. Highlight how you translated business needs into effective data science solutions, showcasing your ability to bridge gaps between technical and non-technical stakeholders.
