Data Scientist

Role Overview:

We are looking for an experienced Data Scientist with strong expertise in Machine Learning, Python, and statistical modelling to develop, deploy, and scale data-driven solutions.

The ideal candidate will have hands-on experience building predictive models, solving complex business problems, and deploying production-ready ML solutions.

Key Responsibilities:

- Design, develop, and deploy machine learning models for classification, regression, forecasting, and predictive analytics.

- Build scalable ML solutions using Python and libraries such as Scikit-learn, TensorFlow, and PyTorch.

- Perform data preprocessing, feature engineering, exploratory data analysis, and statistical analysis.

- Develop and optimize algorithms to improve model accuracy, performance, and scalability.

- Apply supervised, unsupervised, and deep learning techniques to solve business problems.

- Deploy and monitor machine learning models in production environments.

- Build end-to-end ML pipelines and collaborate with data engineering teams for data integration.

- Use model evaluation techniques, validation methods, and performance metrics to ensure reliability.

- Collaborate with business and cross-functional teams to translate requirements into actionable data science solutions.

- Follow software engineering best practices and Agile delivery methodologies.

Mandatory Skills:

- 8+ years of experience in Data Science, Machine Learning, or related fields.

- Strong hands-on expertise in Python programming.

- Excellent understanding of machine learning algorithms, statistical modelling, and predictive analytics.

- Experience with Scikit-learn, TensorFlow, PyTorch, or similar ML frameworks.

- Strong knowledge of data preprocessing, feature engineering, model evaluation, and optimization.

- Experience developing and deploying production-grade machine learning models.

- Proficiency in SQL and working with large datasets.

- Strong analytical, problem-solving, and communication skills.

- Experience working in an Agile environment.

Good to Have:

- Experience with deep learning, NLP, or Generative AI.

- Exposure to cloud platforms such as GCP, AWS, or Azure.

- Experience with MLOps, model monitoring, CI/CD, and ML pipeline automation.

- Knowledge of big data technologies such as Spark and Databricks.

- Experience building scalable enterprise-level ML solutions.