At a Glance
- Tasks: Lead the design and delivery of AI and machine learning solutions for enterprise-scale projects.
- Company: Join Sedona Digital, a fast-growing tech scale-up in Romania.
- Benefits: Competitive salary, career growth, and a collaborative work culture.
- Other info: Dynamic environment with opportunities to work on cutting-edge AI technologies.
- Why this job: Make a real impact by transforming business challenges into data-driven solutions.
- Qualifications: 10 years in data technology, with 5 years as a senior data scientist.
The predicted salary is between 72000 - 88000 £ per year.
Description
Accelerate your development and exposure to high‑performance data platforms and cloud infrastructure.
Join Sedona Digital, a fast‑growing scale‑up with the ambition to be recognised as one of the leading technology companies in Romania.
Our global client base needs builders, engineers who enjoy designing and implementing scalable data platforms, have deep expertise in cloud data technologies, and take pride in delivering reliable, well‑governed solutions.
At Sedona, we
- Obsess about our customers
- Build robust, scalable technical solutions
- Create an open, collaborative culture
- Invest in learning and long‑term careers
We are seeking a Lead Data Scientist to lead the design and delivery of enterprise-scale AI, machine learning, and advanced analytics solutions.
This role combines technical leadership, solution architecture, and hands-on data science expertise to help clients transform business challenges into measurable outcomes through data and AI.
You will define best practices across the Data & AI lifecycle, architect modern Data for AI platforms, design governance and metadata frameworks, and lead multidisciplinary teams delivering scalable analytical and AI solutions.
Working closely with business stakeholders, architects, engineers, and client leadership, you will translate complex requirements into practical, high-value solutions leveraging cloud-native AI and data technologies.
Responsibilities
- Leadership of a data science or data & AI team/function
- Setup and own best practices for a wide range of applied data science and data for AI techniques
- Act as a consulting data architect for data science or data for AI framework design and implementation
- Design and leverage data services to solve enterprise analytical or AI requirements including governance metadata e. g. managed RAG, cataloguing, lineage, trust, weighting, usage, history, obsolescence, observability summarisation for risk, compliance, finops & UX.
- Translate business problems into analytical solutions, identifying opportunities for predictive modelling, optimisation, and data-driven decision-making
- Design, develop, and deploy machine learning models using techniques such as classification, regression, clustering, and forecasting
- Leverage LLM analytical capabilities by engineering prompts to securely hosted AI models
- Apply statistical methods and experimentation techniques (hypothesis testing, A/B testing) to validate models and insights
- Conduct exploratory data analysis (EDA) to quantify data asset value, identify patterns, trends, and key drivers within large datasets
- Engineer features and prepare datasets to improve model performance and robustness
- Evaluate and optimise models using appropriate metrics, cross-validation, and tuning strategies
- Ensure model explainability and interpretability, communicating results clearly to both technical and non-technical stakeholders
- Design and implement MLOps practices including model versioning, monitoring, and retraining strategies
- Collaborate with data engineers to access, prepare, and scale datasets from cloud platforms
- Present insights and recommendations through compelling storytelling and data visualisation (MI/BI)
- Contribute to the design of analytics and AI solutions, focusing on delivering business value rather than infrastructure
- Engage with stakeholders and clients during discovery, experimentation, and solution design phases
Requirements
- 10 years’ working in data-orientated enterprise technology delivery or architecture including
- 5 years’ working as a senior data scientist or engineer delivering DS, ML or Advanced Analytics.
- 2 years’ working with GCP data technologies.
• Hands-on experience with
- Machine Learning techniques (regression, classification, clustering, time series, etc.)
- Statistical analysis and modeling with production deployments
- End-to-end ML lifecycle (data preparation, modeling, evaluation, deployment, monitoring)
- Model performance tuning and validation techniques
- SQL skills and experience working with large datasets
- AI metadata service design and engineering
- Demonstrable, proven ability to lead data teams from design to iterative program delivery and team management.
- Demonstrable, proven ability to elicit, analyse, and document requirements and processes.
- Demonstrable, proven ability with applied data techniques including identification, pipelining/ETL, curation, chunking, modelling, data quality, cataloguing, lineage, package deployment.
- Hands-on experience with Agile methodologies and active participation in Agile ceremonies (e. g., sprint planning, retrospectives, backlog grooming).
- Self-motivated with the ability to work independently and own activities to lead a small, multidisciplinary team.
- Strong problem-solving skills and attention to detail with the ability to work independently and make pragmatic decisions
- Ability to communicate complex data opportunities, AI and analytical concepts clearly to business stakeholders up to C-level.
- Comfortable working in a fast-paced, changing environment.
- Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field
- Preferred Skills (Nice to Have)
- Experience with Generative AI, RAG, and Agentic AI solutions.
- Experience working within Banking, Financial Services or Insurance is highly preferred.
- Knowledge of AI governance, metadata management, and data cataloguing practices.
- Experience within insurance or other regulated industries.
- Exposure to multi-cloud data and AI platforms.
- Experience supporting client-facing workshops, solution design, and pre-sales activities.
- Relevant Data, AI, or Cloud certifications.
- Key Tools & Technologies
- GCP Data & AI Components
- Dataflow
- Dataproc
- Big Query & ML
- Dataplex & Catalogue
- Looker
- Vertex AI Agents & Search
- Gemini
- Azure Data & AI Component
- ADF
- Synapse & Pipelines
- Azure ML
- Databricks
- Purview
- Power BI
- Azure GPT or Claude
- Miro, Figma
- Python, SQL
- CI/CD with Jira, Azure Dev Ops, Git Repos
Lead Data Scientist employer: Sedona Digital
Sedona Digital is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong emphasis on employee growth, you will have access to cutting-edge projects in AI and data solutions, alongside opportunities for professional development and advancement within a fast-growing technology services provider. Join us to be part of a team that values your expertise and encourages meaningful contributions to transformative data strategies for our clients.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist
✨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 Sedona Digital!
✨Show Off Your Projects
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 Lead Data Scientist at Sedona Digital.
✨Leverage Professional Networks
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 Sedona Digital.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Scientist at Sedona Digital, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Data Scientist
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 Sedona Digital, 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 Sedona Digital. 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 Sedona Digital
✨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 Sedona Digital!
✨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.
