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