Senior Data Scientist

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Requirements:

• A Bachelor’s degree in Data Science, Computer Science, Mathematics, Engineering or a related field. Advanced degrees are a plus.
• 5+ years (preferred 8+ years) of proven experience as a data scientist, with expertise in machine learning, statistical analysis, and data visualization.
• Strong proficiency in Python (including libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and TensorFlow/PyTorch) and SQL.
• Strong understanding of data modelling, feature engineering, and model evaluation techniques.
• Experience with experimental design (e.g. A/B testing).
• Experience with data visualisation tools (e.g. Power BI, Tableau) is preferred.
• Familiarity with cloud platforms (e.g. AWS, GCP, Azure) and big data tools (e.g. Spark, Databricks).
• Experience leading and mentoring junior data scientists and other team members
• Experience contributing to and shaping internal data science standards and best practices.
• Strong problem-solving and critical thinking skills and the ability to work with cross-functional teams to deliver results.
• An innovative, collaborative mindset with excellent communication skills to work across teams.

Responsibilities:

• Conduct data cleaning and exploratory data analysis (EDA) to uncover patterns, trends, and actionable insights from complex datasets.
• Develop, deploy and monitor machine learning models and algorithms to solve business challenges and enhance operational efficiency.
• Design experiments and statistical tests to validate hypotheses and measure the impact of implemented solutions.
• Knowledge of MLOps and deploying models to production environments.
• Familiarity with version control systems like Git and collaborative workflows.
• Collaborate with data engineers to understand the data architecture and ensure seamless data access, quality, and transformation pipelines.
• Translate business problems into analytical solutions and communicate effectively to non-technical stakeholders.
• Develop and enforce internal data science standards and best practices.
• Stay updated on the latest developments in AI, machine learning, and industry-specific analytics and investigate proof of concept ideas for adoption in the data science team.
• Serve as a technical leader, coach and mentor for junior team members and adjacent data and engineering teams.
• Lead end-to-end data science projects that support strategic goals. This includes requirements gathering, technical deliverable planning, output quality control and stakeholder management.
• Contribute to the development of the IOB data strategy.