Senior Data Scientist

• Advanced Analytics and Model Development: Design, develop, and deliver sophisticated data products, including problem definition, data acquisition, data exploration and visualization, feature engineering, algorithm experimentation, machine learning model development, evaluation, and deployment.

• Innovation and Prototyping: Drive innovation by rapidly prototyping proof-of-concept ideas and converting them into enterprise solutions.

• Large Language Models (LLMs): Develop and implement advanced LLMs for various applications, enhancing the company s capability in natural language processing and understanding.

• Forecasting: Implement advanced forecasting techniques to predict future trends and behaviours, providing valuable insights for decision-making.

• Customer Lifecycle Management: Analyse customer data to optimize customer lifecycle management strategies, enhancing customer engagement and retention.

• Intelligent Products: Develop intelligent products that leverage AI and machine learning to deliver enhanced customer experiences and operational efficiencies.

• Stakeholder Management: Produce detailed reports and presentations to effectively communicate findings and recommendations to stakeholders at various levels.

• Infrastructure Management: Oversee the management and optimization of our AWS infrastructure to ensure robust, scalable, and cost-effective data solutions.

• Mentorship and Team Development: Mentor junior data scientists, fostering a culture of continuous learning and professional growth within the team .

Team Working

• Actively contribute to the Data Science team dynamics and improvements.

• Engage in internal Business Intelligence and Analytics communities to share knowledge and improve team processes.

• Provide regular and accurate reports of progress to technical leads and the Project lead where required.

• Build strong relationships with stakeholders to provide high-value solutions within the business while keeping communication channels open.

• Maintain strong technical awareness and familiarity with new and upcoming technologies around Data Integration and Business Intelligence Analysis.

• Be prepared to give presentations or provide mentoring on any new technology or skills acquired in a collegiate environment.

• Stay abreast of industry trends and participate in external communities to keep up-to-date and offer informed positions when defining or consulting on solution design.

• Knowledge:

• Qualifications

• Preferred Qualifications

• Extensive knowledge of data science techniques, including data preparation, exploration, and visualization.
  

• In-depth understanding of data mining techniques in one or more areas of statistical modelling methods, time series, text mining, optimization, information retrieval.
  

• Proven ability to produce workflows using classification, clustering, regression, and dimensionality reduction.
  

• Expertise in prototyping and applying statistical analysis and modelling algorithms to solve complex problems in new domains.
  

• Bachelor s or master s degree in computer science, Data Science, Machine Learning, or a related field. Ph.D. is a plus.

• 5+ years of experience in machine learning, data science, or related roles, with a strong track record of developing Data Science powered data products in an agile environment.

• Proficiency in programming languages such as Python, R with a focus on machine learning libraries and frameworks (e.g., TensorFlow, Py-Torch, scikit-learn).

• Extensive experience with SQL and related relational databases.

• Strong understanding of statistical analysis, data mining, and predictive modelling techniques.

• Excellent problem-solving skills and the ability to think critically and creatively.

• Strong communication and collaboration skills, with the ability to work effectively in a team-oriented environment.

• Proven ability to manage business stakeholders, translate business needs into technical requirements, and deliver impactful solutions.

• Experience with version control systems (e.g., Git) and agile development methodologies.
  

• Experience of working on projects centred around Forecasting, Customer Lifecycle Management and Dynamic Pricing.

• Experience of taking projects from prototype to delivery stages while operating in agile development methodologies.

• Experience of working with AWS Technologies Storage (RDBMS, S3, Redshift etc.), Compute (EC2, Lambda, Kinesis, EMR etc.) and Data Science related managed services (Sagemaker, AWS Forecast, Bedrock etc.).