Lead Data Scientist

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

• BigQuery & ML

• Dataplex & Catalogue

• Looker

• Vertex AI Agents & Search

• Gemini

• Azure Data & AI Component

• ADF

• Synapse & Pipelines

• AzureML

• Databricks

• Purview

• Power BI

• AzureGPT or Claude

• Miro, Figma

• Python, SQL

• CI/CD with Jira, Azure DevOps, Git Repos

Originally posted on Himalayas