Role Overview:

We are looking for a Data Scientist / AI-ML Engineer with strong experience in statistical modeling, machine learning, data analysis, and predictive analytics. The candidate will develop, evaluate, and deploy machine learning models to solve business problems, improve decision-making, and deliver measurable business outcomes.

Key Responsibilities:

- Analyze structured and unstructured datasets to identify patterns, trends, and actionable insights.

- Develop, train, validate, and optimize machine learning models for classification, regression, clustering, forecasting, and anomaly detection.

- Apply statistical analysis, hypothesis testing, feature engineering, and exploratory data analysis.

- Build predictive models and translate business requirements into data science solutions.

- Develop data preprocessing and transformation pipelines for model training and evaluation.

- Evaluate model performance using appropriate metrics and validation techniques.

- Implement machine learning solutions using Python and relevant data science libraries.

- Collaborate with data engineers, software developers, product managers, and business stakeholders to operationalize models.

- Support deployment of models into production environments and monitor their performance.

- Identify model drift, data quality issues, and performance degradation, and implement corrective measures.

- Document model assumptions, experiments, methodologies, and results.

- Follow best practices for reproducibility, version control, data governance, and responsible AI.

Required Skills:

- Strong proficiency in Python for data analysis and machine learning.

- Experience with libraries such as pandas, NumPy, scikit-learn, and SciPy.

- Strong understanding of statistics, probability, linear algebra, and machine learning algorithms.

- Hands-on experience with supervised and unsupervised learning techniques.

- Experience with feature engineering, model selection, hyperparameter tuning, and model evaluation.

- Working knowledge of SQL and data extraction from relational databases.

- Familiarity with data visualization tools such as Matplotlib, Seaborn, or Plotly.

- Understanding of data cleaning, missing-value treatment, outlier detection, and feature selection.

- Exposure to deep learning frameworks such as PyTorch or TensorFlow is desirable.

- Familiarity with Git, Jupyter notebooks, and collaborative development practices.

- Basic understanding of model deployment, APIs, and production machine learning workflows.

Preferred Qualifications:

- Exposure to NLP, computer vision, recommendation systems, or time-series forecasting.

- Familiarity with cloud-based machine learning services on AWS, Azure, or GCP.

- Exposure to MLflow, experiment tracking, model versioning, or similar tools.

- Understanding of model explainability, bias assessment, and responsible AI practices.

Key Competencies:

- Analytical thinking, statistical reasoning, experimentation, problem-solving, effective communication, and the ability to translate data into practical business solutions.