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
