Data Scientist - AI / ML

Pune

About Us

We empower enterprises globally through intelligent, creative, and insightful services for data integration, data analytics and data visualization.

Hoonartek is a leader in enterprise transformation, data engineering and an acknowledged world-class Ab Initio delivery partner.

Using centuries of cumulative experience, research and leadership, we help our clients eliminate the complexities & risk of legacy modernization and safely deliver big data hubs, operational data integration, business intelligence, risk & compliance solutions and traditional data warehouses & marts.

At Hoonartek, we work to ensure that our customers, partners and employees all benefit from our unstinting commitment to delivery, quality and value. Hoonartek is increasingly the choice for customers seeking a trusted partner of vision, value and integrity

How We Work?

Define, Design and Deliver (D3) is our in-house delivery philosophy. It’s culled from agile and rapid methodologies and focused on ‘just enough design’. We embrace this philosophy in everything we do, leading to numerous client success stories and indeed to our own success.

We embrace change, empowering and trusting our people and building long and valuable relationships with our employees, our customers and our partners. We work flexibly, even adopting traditional/waterfall methods where circumstances demand it. At Hoonartek, the focus is always on delivery and value.

Job Description

Key Responsibilities

• Build, train, and deploy machine learning models for predictive analytics, optimization, and business intelligence use cases.
• Perform Exploratory Data Analysis (EDA) to uncover patterns, trends, and actionable insights from structured and large-scale datasets.
• Develop feature engineering pipelines and feature stores to enable scalable and reusable ML workflows.
• Design end-to-end data science solutions covering data preparation, model development, validation, and deployment.
• Implement MLOps practices to manage model lifecycle including versioning, monitoring, and continuous improvement.
• Work with Databricks and Spark environments for large-scale data analysis and model development.
• Deploy and manage ML models using GCP services such as Vertex AI or similar cloud ML platforms.
• Collaborate with business stakeholders, data engineers, and analytics teams to translate business problems into AI-driven solutions.
• Evaluate and improve model performance using experimentation, hyperparameter tuning, and validation techniques.

Core Skills Required

• Strong hands-on experience in Machine Learning, Statistical Modeling, and Predictive Analytics.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) for data analysis and model development.
• Experience working with Databricks (DBx) and Spark / PySpark environments.
• Hands-on experience with MLOps frameworks and model lifecycle management.
• Experience deploying or managing ML models on GCP (Vertex AI) or similar cloud ML platforms.
• Strong knowledge of Feature Engineering, Feature Store concepts, and ML experimentation.
• Expertise in EDA, model evaluation, and performance optimization techniques.

Good to Have

• Experience with MLflow, Databricks Feature Store, or model monitoring tools.
• Exposure to CI/CD pipelines for ML deployment.
• Experience working with large-scale distributed datasets.
• Domain knowledge in BFSI, fraud analytics, risk modeling, or customer analytics.

Job Requirement

Key Responsibilities

• Build, train, and deploy machine learning models for predictive analytics, optimization, and business intelligence use cases.
• Perform Exploratory Data Analysis (EDA) to uncover patterns, trends, and actionable insights from structured and large-scale datasets.
• Develop feature engineering pipelines and feature stores to enable scalable and reusable ML workflows.
• Design end-to-end data science solutions covering data preparation, model development, validation, and deployment.
• Implement MLOps practices to manage model lifecycle including versioning, monitoring, and continuous improvement.
• Work with Databricks and Spark environments for large-scale data analysis and model development.
• Deploy and manage ML models using GCP services such as Vertex AI or similar cloud ML platforms.
• Collaborate with business stakeholders, data engineers, and analytics teams to translate business problems into AI-driven solutions.
• Evaluate and improve model performance using experimentation, hyperparameter tuning, and validation techniques.

Core Skills Required

• Strong hands-on experience in Machine Learning, Statistical Modeling, and Predictive Analytics.
• Proficiency in Python (Pandas, NumPy, Scikit-learn) for data analysis and model development.
• Experience working with Databricks (DBx) and Spark / PySpark environments.
• Hands-on experience with MLOps frameworks and model lifecycle management.
• Experience deploying or managing ML models on GCP (Vertex AI) or similar cloud ML platforms.
• Strong knowledge of Feature Engineering, Feature Store concepts, and ML experimentation.
• Expertise in EDA, model evaluation, and performance optimization techniques.

Good to Have

• Experience with MLflow, Databricks Feature Store, or model monitoring tools.
• Exposure to CI/CD pipelines for ML deployment.
• Experience working with large-scale distributed datasets.
• Domain knowledge in BFSI, fraud analytics, risk modeling, or customer analytics.