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For Companies At a Glance
- Tasks: Lead complex analytics projects and deliver bespoke data science solutions.
- Company: Join a world-leading private equity firm at the forefront of analytics and investment.
- Benefits: Enjoy a hybrid work model with a competitive salary up to £130,000.
- Why this job: Be part of an elite team driving impactful decisions in a fast-paced environment.
- Qualifications: Strong STEM background with proven data science experience and advanced Python/SQL skills.
- Other info: This is a hands-on role, perfect for those who love coding and influencing billion-dollar decisions.
The predicted salary is between 78000 - 182000 £ per year.
Location: London (Hybrid)
Compensation: Up to £130,000
Are you a hands-on data scientist who thrives in fast-paced, high-performance environments? We’re working with a world-leading private equity firm that sits at the intersection of cutting-edge analytics and strategic investment. They are seeking a Data Science Manager—someone who combines exceptional technical depth with commercial insight to influence billion-dollar decisions. This is not a people management role—this is a hands-on, sleeves-rolled-up position, ideal for someone who loves to code, lead from the front, and drive impact through data. You’ll be a pivotal part of a small, elite team responsible for delivering analytical firepower across deal sourcing, due diligence, and post-acquisition value creation.
What You’ll Be Doing
- Leading complex analytics projects across portfolio companies and investment opportunities.
- Scoping and delivering bespoke data science solutions that inform high-stakes decision-making.
- Acting as a technical lead and mentor for junior contributors and contractors.
- Building reusable IP: dashboards, codebases, tooling, and frameworks.
- Collaborating with C-level execs and deal teams on high-impact commercial initiatives.
What We’re Looking For
- A strong academic background in a STEM field (Master’s preferred).
- Proven experience in data science, machine learning, or analytics in consulting, PE, or financial services.
- Advanced Python and SQL skills; exposure to tools like Snowflake, Databricks, Power BI/Tableau.
- A commercial mindset—someone who can connect analytical output with business outcomes.
- Clear communicator who builds trust with senior stakeholders and thrives in ambiguity.
Data Science Manager employer: TechNET IT Recruitment Ltd
Contact Detail:
TechNET IT Recruitment Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Manager
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, such as Snowflake and Databricks. Having hands-on experience or projects showcasing your skills with these platforms can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the private equity and financial services sectors. Attend industry events or webinars to connect with potential colleagues or decision-makers who might influence the hiring process.
✨Tip Number 3
Prepare to discuss real-world examples of how your data science work has driven business outcomes. Be ready to articulate your thought process and the impact of your analytical solutions on previous projects.
✨Tip Number 4
Showcase your ability to communicate complex data insights clearly. Practice explaining your past projects to non-technical stakeholders, as this will demonstrate your capability to bridge the gap between data and business strategy.
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, machine learning, and analytics. Emphasise your technical skills in Python and SQL, and any exposure to tools like Snowflake or Power BI. Tailoring your CV to reflect the specific requirements of the Data Science Manager role will make you stand out.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and how your hands-on experience aligns with the responsibilities of the role. Mention specific projects where you've influenced decision-making through data, showcasing your commercial mindset and ability to communicate effectively with senior stakeholders.
Highlight Leadership Experience: Even though this is not a people management role, it's important to demonstrate your ability to lead complex analytics projects. Include examples of how you've acted as a technical lead or mentor in previous roles, and how you've collaborated with C-level executives on impactful initiatives.
Showcase Problem-Solving Skills: Provide examples in your application that illustrate your problem-solving abilities in high-stakes environments. Discuss how you've scoped and delivered bespoke data science solutions that have driven significant business outcomes, reinforcing your fit for the role.
How to prepare for a job interview at TechNET IT Recruitment Ltd
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, SQL, and any relevant tools like Snowflake or Databricks. You might be asked to solve a technical problem on the spot, so brush up on your coding skills and be ready to demonstrate your analytical thinking.
✨Connect Data Science to Business Outcomes
Since the role requires a commercial mindset, think about how your previous projects have influenced business decisions. Be ready to share specific examples where your data-driven insights led to significant outcomes, especially in high-stakes environments.
✨Prepare for Scenario-Based Questions
Expect questions that assess your ability to lead complex analytics projects. Prepare to discuss how you would approach a hypothetical project, including scoping, delivering solutions, and collaborating with stakeholders. This will showcase your strategic thinking and problem-solving skills.
✨Demonstrate Your Communication Skills
As a clear communicator, you’ll need to build trust with senior stakeholders. Practice explaining complex data concepts in simple terms. Be ready to discuss how you’ve effectively communicated findings to non-technical audiences in the past.
