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About the Role
We are seeking a Data Scientist (Machine Learning) to develop and deploy advanced analytics and machine learning solutions that support business operations and digital transformation initiatives. The successful candidate will work closely with cross-functional teams to analyze large datasets, build predictive models, and deliver actionable insights to improve operational efficiency and business performance.
Responsibilities
- Develop, train, validate, and deploy machine learning models for predictive analytics and optimization.
- Analyze structured and unstructured datasets to identify trends, patterns, and business opportunities.
- Design and implement data pipelines for data collection, cleansing, feature engineering, and model training.
- Build forecasting, classification, regression, clustering, and anomaly detection models.
- Collaborate with business stakeholders to understand requirements and translate them into data-driven solutions.
- Evaluate model performance and continuously improve model accuracy and reliability.
- Develop dashboards and reports to communicate insights and recommendations.
- Work with data engineers to integrate machine learning models into production systems.
- Ensure data quality, governance, and compliance with organizational standards.
- Research and evaluate new machine learning algorithms and emerging technologies.
Requirements
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
- 3–8 years of experience in Data Science, Machine Learning, or Advanced Analytics.
- Strong programming skills in Python (Pandas, NumPy, Scikit-learn).
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or XGBoost.
- Strong knowledge of supervised and unsupervised learning techniques.
- Experience with SQL and relational databases.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Experience with data visualization tools such as Power BI or Tableau.
- Knowledge of Git, Docker, and MLOps concepts is an advantage.
- Strong analytical, problem-solving, and communication skills.
Skills
- Time-series forecasting
- Predictive maintenance
- Optimization techniques
- Operations research
- Big data technologies (Spark, Hadoop)
- Generative AI
- Large Language Models (LLMs)
- Utilities sector experience
- Energy sector experience
- Manufacturing sector experience
- Industrial sectors experience
Experience Level
- 3-8 years
Education Level
- Bachelor's degree
- Master's degree