At a Glance
- Tasks: Dive into customer analytics, build models, and create dashboards that drive business decisions.
- Company: Join HENI, a cutting-edge art services business blending art and technology.
- Benefits: Enjoy competitive pay, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects and career advancement potential.
- Why this job: Make a real impact in the art world by leveraging data to enhance accessibility.
- Qualifications: PhD in a quantitative field and 1-2 years of relevant experience required.
The predicted salary is between 50000 - 70000 £ per year.
HENI is an international art services business working with leading artists and estates across printmaking, marketplaces for physical artworks, NFTs, publishing, digital, video production, art research and analysis. HENI is at the cutting edge of art and tech using the latest and best technologies, to make art accessible to audiences worldwide.
We are looking for a Data Scientist with a strong academic foundation to join our Data team. You will apply your research skills to customer analytics — building models, creating dashboards, and delivering insights that support commercial teams and C-suite decision-making. You will also play a key role in broader data initiatives across the organisation, working to shape and deliver cross-team projects.
Key Responsibilities- Design and execute customer analytics: segmentation models, retention analysis, and behavioral insights
- Create and maintain Customer Data Reports for C-suite stakeholders covering key business metrics
- Build and maintain dashboards in Apache Superset for self-serve business intelligence
- Write analytic SQL queries to support accounts and client liaison teams
- Integrate third-party data sources (e.g. HubSpot, Facebook Business) into the customer data platform
- Build and maintain data pipelines
- Develop internal data applications for ad-hoc analysis and customer research
- Implement data quality checks and validation to ensure pipeline reliability
- Support data architecture decisions and contribute to broader data platform improvements
- Respond to ad-hoc data requests from across the business
- Contribute to HENI News data initiatives
- Strong Python skills — the primary language for all data work pandas and numpy for exploratory analysis and smaller datasets
- SQL for analytical queries and database interaction
- Strong foundation in statistical modelling and/or machine learning (e.g. scikit-learn, scipy, statsmodels)
- Experience with data visualisation libraries (matplotlib, seaborn, or similar)
- Experience working with REST APIs
- Experience with BI/dashboarding tools (e.g. Superset, Looker, Metabase)
- Experience building internal data tools or apps (e.g. Streamlit, Dash)
- Git and version control workflows
- Familiarity with automated testing approaches: unit tests, integration tests, and data quality tests
- Familiarity with Infrastructure as Code, containerization (Docker), CI/CD
- Writing clean, maintainable, well-structured code
- Experience with distributed data processing frameworks (e.g. PySpark, Spark SQL)
- Experience with cloud-based data pipeline tools (e.g. AWS Glue, Azure Data Factory, GCP Dataproc)
- Experience with container orchestration (e.g. Kubernetes, Docker swarm, AWS ECS)
- Familiarity with cloud object storage (e.g. S3, GCS, Azure Blob) and columnar data formats (e.g. Parquet)
- Experience with CRM/marketing platform APIs (e.g. HubSpot, Salesforce or similar)
- Experience integrating LLM APIs (e.g. Gemini/Vertex AI, OpenAI/ChatGPT) to build sophisticated data products
- AWS (S3, RDS, Glue, ECS, EC2)
- Streamlit, Apache Superset
- Git
- SQL (strong)
- PhD in a quantitative discipline (e.g. Statistics, Computer Science, Physics, Mathematics, Engineering, or related field)
- 1-2 years of industry experience in a data science, analytics, or software role
- Ability to translate academic research skills into practical business insights
- Experience presenting data insights to non-technical stakeholders
- Eager to learn production data engineering practices and cloud tooling
Data Scientist in London employer: HENI
HENI is an exceptional employer that fosters a dynamic and innovative work culture at the intersection of art and technology. As a Data Scientist, you will have the opportunity to work with cutting-edge tools and technologies while contributing to meaningful projects that enhance the accessibility of art worldwide. With a strong emphasis on employee growth, HENI offers a collaborative environment where your insights directly influence decision-making at the highest levels, ensuring a rewarding and impactful career path.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like HENI!
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Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist at HENI.
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like HENI.
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We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at HENI, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at HENI. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at HENI
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at HENI!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
