Job Description :

Role & responsibilities :

- 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 generalizability.

- 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.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field. PhD is a plus.

- Proven experience (5+ 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 DWH technology (Snowflake) 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, Billing & Invoicing, user behavioural analytics.