Job Summary
We are looking for an experienced Data Scientist / Machine Learning Engineer to design, develop, and deploy scalable machine learning and AI solutions. The ideal candidate will have strong hands-on experience in Python, SQL, modern ML frameworks, cloud platforms, MLOps, Generative AI, and knowledge-based AI technologies.
This role will work closely with data, engineering, and business teams to build intelligent solutions for retail, personalization, and customer-facing digital applications.
Key Responsibilities
Design, develop, train, and optimize machine learning models for real-world business use cases.
Build scalable data and ML pipelines using Python, SQL, and modern machine learning frameworks.
Develop and maintain production-ready ML solutions using TensorFlow, PyTorch, and Scikit-Learn.
Implement MLOps practices for model training, deployment, monitoring, and lifecycle management.
Work with cloud platforms such as GCP and AWS to develop and deploy scalable AI/ML solutions.
Apply statistical analysis, probability, experimentation, and A/B testing to evaluate models and business outcomes.
Develop solutions leveraging LLMs and Generative AI, including technologies such as GPT, BERT, LLaMA, or Claude.
Work with Knowledge Graphs and Neo4j to enhance AI/ML applications and integrate structured knowledge into intelligent systems.
Collaborate with software engineers and data engineers to integrate ML models into production applications.
Contribute to Agile and DevOps practices, including code reviews, CI/CD, testing, and production support.
Develop data visualizations and business intelligence solutions to communicate insights to technical and business stakeholders.
Work on retail and personalization use cases, including customer behavior, recommendations, and digital/web experiences.
Required Qualifications
7+ years of experience in Data Science, Machine Learning, AI, or a related field.
Strong hands-on experience with Python and SQL.
Experience with TensorFlow, PyTorch, and/or Scikit-Learn.
Practical experience with MLOps and tools such as MLflow, Kubeflow, or Airflow.
Strong experience with GCP and/or AWS.
Solid understanding of statistics, probability, experimentation, and A/B testing.
Hands-on experience with LLMs and Generative AI.
Experience with Knowledge Graphs and Neo4j.
Experience building scalable data/ML pipelines and deploying models into production.
Understanding of Agile, DevOps, CI/CD, and collaborative software engineering practices.
Experience with BI and data visualization tools.
Bachelor's degree in Computer Science, Mathematics, Engineering, Data Science, or a related field, or equivalent practical experience.
Preferred Experience
Experience in retail, e-commerce, personalization, recommendation systems, or digital/web technologies.
Experience integrating Knowledge Graphs with ML/AI applications.
Experience with production-scale Generative AI applications.
Experience working in cross-functional teams involving data science, engineering, product, and business stakeholders.
Key Skills
Data Science | Machine Learning | Python | SQL | TensorFlow | PyTorch | Scikit-Learn | MLOps | MLflow | Kubeflow | Airflow | GCP | AWS | Generative AI | LLM | Knowledge Graph | Neo4j | A/B Testing | Statistics | Retail | Personalization | BI | Data Visualization | Agile | DevOps