Senior Data Scientist

Our client is seeking a Senior Data Scientist to lead the development of advanced analytics, machine learning models, and data-driven solutions that support strategic business decision-making. The successful candidate will work closely with business stakeholders, engineering teams, and leadership to extract meaningful insights from complex datasets.

Requirements

Key Responsibilities

Design and implement advanced predictive models. Develop machine learning and statistical solutions. Analyse large and complex datasets. Build forecasting, classification, and recommendation models. Drive data-driven decision-making across business functions. Present insights and recommendations to stakeholders.

Mentor junior Data Scientists and Analysts. Collaborate with Data Engineering teams to build scalable data solutions. Deploy and monitor machine learning models in production. Minimum Requirements Bachelor's Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field. Master's Degree preferred.

5+ years of Data Science experience. Proven experience delivering machine learning solutions in production environments. Technical Skills Essential Python SQL Machine Learning Statistics Predictive Analytics Data Modelling Data Visualisation Feature Engineering Tools & Technologies Python Pandas Num. Py Scikit-Learn Tensor.

Flow or Py. Torch SQL Server PostgreSQL Power BI Tableau Jupyter Notebooks Cloud Platforms AWS Azure Google Cloud Platform Preferred Experience Generative AI Natural Language Processing (NLP) MLOps Data Engineering exposure Big Data technologies Spark Databricks Key Competencies Advanced Analytical Skills Business Acumen Problem Solving Communication Skills Leadership & Mentoring Stakeholder Management Preferred Certifications AWS Certified Machine Learning Microsoft Azure Data Scientist Associate Google Professional Machine Learning Engineer Databricks Certifications Success Measures Delivery of business value through analytics.

Accuracy and performance of predictive models. Stakeholder satisfaction. Successful deployment of production-ready solutions. Continuous improvement of data-driven capabilities.