Description

Key Responsibilities

• Design, develop, and deploy machine learning, deep learning, and statistical models to solve complex business problems.
• Analyze structured and unstructured data to identify trends, patterns, and actionable insights.
• Build predictive models, recommendation systems, forecasting models, and classification algorithms.
• Develop data pipelines and feature engineering processes for model training and deployment.
• Collaborate with product managers, business stakeholders, data engineers, and software developers to define data science solutions.
• Perform exploratory data analysis (EDA), hypothesis testing, and statistical analysis.
• Optimize machine learning models for scalability, accuracy, and performance.
• Deploy ML models using MLOps best practices and monitor model performance in production.
• Communicate analytical findings and recommendations through dashboards, reports, and presentations.
• Mentor junior data scientists and promote data science best practices across the organization.
• Stay current with emerging AI, machine learning, and data science technologies.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (Ph.D. preferred).
• 6+ years of experience in Data Science, Machine Learning, or Artificial Intelligence.
• Strong proficiency in Python or R for data analysis and machine learning.
• Experience with SQL and relational databases.
• Expertise in machine learning algorithms, statistical modeling, and predictive analytics.
• Experience with large datasets and distributed computing frameworks.
• Strong understanding of probability, statistics, linear algebra, and optimization techniques.
• Experience deploying machine learning models into production environments.
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications

• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI agents.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Knowledge of data visualization tools such as Tableau, Power BI, or Plotly.
• Experience working in Agile/Scrum environments.

Technical Skills

• Programming: Python, R, SQL
• Machine Learning: Scikit-learn, XGBoost, LightGBM, CatBoost
• Deep Learning: TensorFlow, PyTorch, Keras
• Generative AI: OpenAI APIs, LangChain, LlamaIndex, Hugging Face Transformers
• Big Data: Apache Spark, Hadoop, Databricks
• Databases: PostgreSQL, MySQL, SQL Server, Snowflake
• Cloud: AWS, Azure, Google Cloud Platform
• MLOps: MLflow, Kubeflow, Docker, Kubernetes
• Visualization: Power BI, Tableau, Matplotlib, Plotly

Key Skills

Education

Any Graduate