Senior 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 looking for a Senior Data Scientist with strong expertise in machine learning, advanced analytics, and statistical modelling to design and deliver data-driven solutions that generate measurable business impact.
The role focuses on translating complex business challenges into analytical and AI-driven solutions, developing robust machine learning models, and communicating insights effectively to stakeholders. Working closely with clients, architects, and data engineers, you will leverage modern cloud-based data and AI platforms to deliver scalable analytics, machine learning, and Generative AI capabilities that support strategic decision-making.
Responsibilities
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 optimize 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
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

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 within a multidisciplinary team.

Strong problem-solving skills and attention to detail with the ability to work independently and make pragmatic decisions

Ability to communicate complex analytical concepts clearly to business stakeholders

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, prompt engineering, Retrieval-Augmented Generation (RAG), or Agentic AI solutions.

Experience working within Banking, Financial Services or Insurance is highly preferred.

Experience working with Vertex AI, Gemini, BigQuery ML, or similar cloud-native AI services.

Familiarity with MLOps frameworks and production model monitoring practices.

Experience with data governance, cataloguing, lineage, or metadata management solutions.

Exposure to data visualisation and BI platforms such as Looker or Power BI.

Experience participating in client workshops, discovery sessions, or solution design activities.

Relevant certifications in Data Science, Machine Learning, AI, or Google Cloud technologies.

Key Tools & Technologies
GCP Data & AI Components

Dataflow

Dataproc

BigQuery & ML

Dataplex & Catalogue

Looker

Vertex AI Agents & Search

Gemini

Miro, Figma

Python, SQL

CI/CD with Jira, Azure DevOps, Git Repos

Originally posted on Himalayas