Data Scientist

Critical Skills to Possess:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field. PhD is a plus.
• Proven experience (4+ years) in building, refining, and deploying machine learning/statistical models in a professional setting.
• Strong programming skills in Python, R, or similar languages, with proficiency in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and model deployment tools (e.g., Docker, Kubernetes, MLflow) is highly desirable.
• Familiarity with big data technologies (e.g., Spark, Hadoop) and database systems (SQL/NoSQL).
• Strong grasp of statistical concepts, hypothesis testing, and experimental design.
• Excellent problem-solving skills, with the ability to break down complex issues into actionable tasks.
• Outstanding communication skills, with the ability to convey complex technical concepts to non-technical audiences.
• Demonstrated ability to thrive in a collaborative, fast-paced environment.

Preferred Qualifications

• Experience in deploying models into real-time or high-availability production environments.
• Familiarity with MLOps practices and tools.
• Knowledge of data visualisation tools (e.g., Tableau, Power BI, Plotly, Dash).
• Experience with CI/CD pipelines for ML projects.
• Domain expertise in areas such as Energy, finance, healthcare, retail, or marketing analytics.
• Experience in the Manufacturing domain is highly preferred.

• Model Building:

   Design and develop robust machine learning and statistical models tailored to solving complex business problems. Select appropriate algorithms, optimise parameters, and rigorously validate models to ensure high performance and reliability.

• Model Refinement:

   Continuously monitor, test, and improve models based on feedback, new data, or changing business requirements. Implement techniques such as hyperparameter tuning, feature engineering, and cross-validation to enhance model accuracy and generalisability.

• Model Deployment:

   Deploy models to production environments, ensuring scalability, stability, and maintainability. Work closely with data engineers and DevOps teams to integrate models into business systems, APIs, or real-time applications.

• Data Analysis & Exploration:

   Analyse large and complex datasets to uncover trends, patterns, and opportunities. Use statistical methods to interpret results and present actionable recommendations to stakeholders.

• Collaboration & Communication:

   Work cross-functionally with business leaders, product managers, analysts, and engineers to understand requirements, translate business needs into analytical solutions, and clearly communicate findings and recommendations.

• Documentation & Best Practices:

   Maintain comprehensive documentation for models, codebases, and analytical processes. Promote and adhere to best practices in coding, experimentation, and reproducibility.

• Innovation & Learning:

   Stay abreast of the latest developments in data science, machine learning, and AI. Proactively identify and evaluate new tools, frameworks, and techniques that can enhance our capabilities.