Data Scientist

The Data Scientist is responsible for leveraging advancedanalytics, machine learning, and statistical modelling to extract actionableinsights from complex datasets. This role supports strategic decision -making,drives innovation, and enhances operational efficiency across the organisation.Key ResponsibilitiesAdvanced Data Analysis & Modelling Develop, implement, and maintain predictive andprescriptive models using machine learning algorithms to forecast businessoutcomes, enabling proactive decision -making and strategic planning.Analyse large and complex datasets using statisticaltechniques to uncover patterns and trends, driving data -informed insights andoperational improvements.Monitor model performance using validation metrics andretrain models as needed to maintain accuracy, ensuring continued relevance andreliability of outputs.Translate business challenges into analytical problemsusing structured frameworks, enabling the development of targeted and effectivedata solutions.Data Engineering & Management    Collaborate with data engineers to build robust datapipelines and ensure data integrity.Maintain and optimize data storage solutions forscalability and performance.Identify opportunities for automation in reporting andanalysis using scripting and APIs, increasing efficiency, and reducingturnaround time.Document methodologies, assumptions, and outcomes in aclear and reproducible format to support transparency, governance, andknowledge sharing.Business Intelligence & Strategic InsightsTranslate complex data into actionable insights thatsupport strategic decision -making.Identify trends, patterns, and anomalies that informbusiness strategies and operational improvements.Develop and maintain dashboards and reports for variousbusiness units.Solution Development & DeploymentBuild end -to -end data science solutions, from prototypeto production.Integrate models into business applications or platformsusing APIs or other deployment methods.Monitor deployed models for performance drift and retrainas necessaryStakeholder Engagement & CommunicationWork closely with business stakeholders to understandrequirements and define analytical approaches.Communicate findings clearly through presentations,visualisations, and storytelling to enhance stakeholder understanding andengagement.Provide training and support to non -technical users ondata tools and insights to build analytical capacity, empowering teams toleverage data independently.Innovation & Continuous ImprovementExperiment with new techniques to improve modelperformance and analytical capabilities fostering innovation and continuousimprovement.Contribute to the development of best practices,standards, and frameworks within the data science team to ensure consistencyand quality.Governance, Compliance & Ethical Use of DataEnsure compliance with data privacy regulations byapplying ethical data managing practices, protecting sensitive information, andmaintaining stakeholder trust.Implement model governance practices includingdocumentation, versioning, and audit trails.Apply bias mitigation techniques in model development toensure fairness, accuracy, and responsible AI practices Role CompetenciesDeep understanding of statistical methods, probabilitytheory, linear algebra, and calculus to support model development and datainterpretation.Advanced proficiency in Python, R, SQL, and familiaritywith Java or Scala. Ability to write clean, efficient, and reusable code.Experience with supervised and unsupervised learning,deep learning frameworks (e.g., TensorFlow, PyTorch), and model evaluationtechniques.Knowledge of data warehousing, ETL processes, and workingwith structured and unstructured data.Familiar with cloud platforms (AWS, Azure, GCP),containerization (Docker, Kubernetes), and scalable data solutions.Skilled in using tools like Power BI, TableauAnalytical & Problem -Solving SkillsAbility to approach problems logically, identify rootcauses, and propose data -driven solutions.Understands business operations and can align datascience initiatives with strategic goals.Continuously seeks new methods, tools, and approaches toimprove analytical outcomes and business impact.Communication & InfluenceCapable of translating complex data findings into clear,compelling narratives for diverse audiences. Builds strong relationships withinternal and external stakeholders, understands their needs, and deliversrelevant insights.Confident in presenting technical content tonon -technical audiences, including executives and decision -makers.Collaboration & Teamwork  Works effectively with product managers, engineers,analysts, and business leaders to co -create solutions.Comfortable working in iterative environments, adaptingto changing priorities and feedback.Adaptability and AgilityDemonstrates the ability to navigate ambiguity withconfidence and composure.Adapts effectively to shifting priorities, evolvinggoals, and dynamic business contexts.Contributes proactively to refining processes,structures, and ways of working to support organisational growth.Brings strong problem -solving skills, flexibility, andresilience, coupled with a learning and growth mindset, to thrive in an agile,high -growth environment. RequirementsBachelor's degree in data science, Computer Science,Statistics, Mathematics, or a related field.3–5 years of experience in data science, analytics, or arelated field.Proven experience with machine learning, predictivemodelling, and statistical analysis.Strong proficiency in Python, R, SQL, and datavisualisation tools (e.g., Power BI, Tableau).Experience with cloud platforms (e.g., AWS, Azure, GCP)and big data technologies (e.g., Spark, Hadoop) is advantageous.Familiar with version control systems (e.g., Git) andcollaborative development practices.AdvantageousMaster's degree in data science, Computer Science,Statistics, Mathematics, or related field.Experience in healthcare, retail, or insurance dataecosystems