KANINI - Data Scientist - Machine Learning

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.

(ref:hirist.tech)