We are seeking a highly motivated and experienced Data Scientist to join our growing team. The ideal candidate will have strong expertise in machine learning, statistical modeling, data engineering, and cloud-based AI/ML solutions. You will work closely with business stakeholders, engineering teams, and data professionals to develop scalable machine learning products and data-driven solutions that drive business impact.

Key Responsibilities

• Perform Exploratory Data Analysis (EDA) to identify trends, patterns, and actionable insights from large datasets.
• Design, build, and optimize machine learning models for various business use cases.
• Develop and maintain feature engineering pipelines, feature selection frameworks, and online/offline feature stores.
• Build and manage batch and real-time streaming data pipelines using modern data platforms.
• Collaborate with business, engineering, infrastructure, and data science teams to translate business challenges into AI/ML solutions.
• Deploy, monitor, and maintain machine learning models in production environments.
• Develop reusable ML components, libraries, CI/CD pipelines, deployment standards, and technical documentation.
• Evaluate model performance using appropriate statistical and machine learning metrics.
• Create compelling visualizations, dashboards, and presentations to communicate findings and recommendations to technical and non-technical stakeholders.
• Support the full machine learning lifecycle, from data collection and experimentation to deployment and monitoring.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 4+ years of experience in Data Science, Machine Learning, or Applied AI.
• Strong proficiency in Python and SQL.
• Experience with Git and software development best practices.
• Hands-on experience with Databricks, PySpark, and Apache Spark.
• Strong understanding of machine learning algorithms, statistical analysis, and model evaluation techniques.
• Experience building and deploying machine learning solutions in Azure Cloud environments.
• Knowledge of MLOps practices, CI/CD pipelines, and model lifecycle management.
• Experience working with large-scale structured and unstructured datasets.

Preferred Qualifications

Experience in one or more of the following areas:

• Time Series Forecasting
• Recommendation Systems
• A/B Testing and Experimentation
• Dynamic Pricing
• Logistics and Supply Chain Optimization
• Churn Prediction
• Customer Segmentation
• Natural Language Processing (NLP)
• Deep Learning
• Reinforcement Learning

Technical SkillsProgramming & Data

• Python
• SQL
• PySpark
• Apache Spark
• Git

Cloud & Infrastructure

• Microsoft Azure
• Kubernetes
• Docker

Machine Learning & AI

• Scikit-learn
• PyTorch
• TensorFlow
• Keras
• MLOps Frameworks

Data Platforms

• Databricks
• Feature Stores
• Real-Time Streaming Architectures

What You'll Bring

• Strong analytical and problem-solving skills.
• Ability to work in a fast-paced, collaborative environment.
• Excellent communication and stakeholder management skills.
• Experience presenting technical concepts and analytical findings to diverse audiences.
• Passion for building scalable AI/ML solutions that deliver measurable business value.

Interview Process

Candidates Should Be Prepared To Demonstrate Proficiency In

• Statistics and Probability
• Machine Learning
• Python Programming
• SQL
• PySpark
• Optimization Techniques
• MLOps and Model Deployment
• Technical Coding Assessments and Take-Home Assignments

Skills: python,pyspark,machine learning,sql