Data Science Specialist

Data Science (Statistical Modelling)

Experience: 6 to 9 yrs, Location: Bengaluru

Role Overview

We are looking for a highly skilled Data Scientist to design, develop, and deploy advanced analytics and machine learning solutions that address complex business problems. The ideal candidate will have strong expertise in statistical modelling, machine learning, deep learning, and cloud-based ML deployments, along with experience working with large-scale data environments.

The candidate will collaborate with cross-functional teams to transform data into actionable insights and develop scalable ML solutions that drive business value.

Key Responsibilities

Data Science & Machine Learning

• Develop and implement statistical models and machine learning algorithms to solve business problems.
• Work on techniques such as time series forecasting, reliability models, Markov models, stochastic models, Bayesian modelling, classification models, clustering, and neural networks.
• Apply deep learning frameworks such as PyTorch, TensorFlow, or Keras for building predictive models.

Data Processing & Feature Engineering

• Perform data preprocessing, cleansing, validation, and transformation of structured and unstructured datasets.
• Design feature engineering pipelines to improve model performance.

Big Data & Distributed Computing

• Work with big data platforms such as Hadoop, Elasticsearch, and distributed computing frameworks.
• Query and process large datasets using SQL, Hive, or other database technologies.

Cloud & Model Deployment

• Build, train, and deploy machine learning models on cloud platforms such as AWS, Azure, or GCP.
• Optimize models for performance, scalability, and reliability.

MLOps & Productionization

• Implement MLOps practices including Dockerization, REST APIs, and CI/CD pipelines for ML model deployment.
• Monitor and maintain ML models in production environments.

Generative AI & Foundation Models

• Adapt foundation models or large language models (LLMs) to solve specific business challenges.
• Integrate AI/ML solutions into enterprise applications and workflows.

Collaboration & Communication

• Work closely with data engineers, analysts, and business stakeholders to translate business problems into data science solutions.
• Communicate insights and technical concepts effectively to both technical and non-technical audiences.

Required Skills & Qualifications

• Strong programming experience in Python or R.
• Proficiency in SQL and database query languages (Hive/Pig desirable).
• Experience with machine learning, statistical modelling, and deep learning techniques.
• Hands-on experience with PyTorch, TensorFlow, or Keras.
• Experience with cloud platforms (AWS, Azure, or GCP) for ML deployment.
• Knowledge of big data technologies such as Hadoop, Elasticsearch, HBase, or Hive.
• Experience with MLOps practices including Docker, REST APIs, and CI/CD pipelines.
• Strong analytical thinking and problem-solving abilities.