<p><p>We are looking for passionate and skilled Data Scientists to join our team and lead the development of cutting-edge Artificial Intelligence solutions tailored for the Retail industry.
The ideal candidate will have hands-on experience building and deploying AI models that enhance customer experience, optimize operations, and drive data-driven decision-making in retail environments.<br/><br/> Key Responsibilities : <br/></p><br/><p>- Design, develop, and implement AI/ML models for retail use cases such as : Dynamic pricing, Dynamic routing, Inventory management, Personalized recommendations</p><p><br/></p><p>- Build scalable AI pipelines and integrate models into production systems using cloud and edge technologies.</p><p><br/></p><p>- Collaborate with data scientists, software engineers, and business stakeholders to translate business needs into technical solutions.</p><p><br/></p><p>- Work on Computer Vision (e.g., shelf analytics, visual search) and NLP (e.g., chatbots, sentiment analysis) projects relevant to retail.</p><p><br/></p><p>- Optimize model performance and ensure robustness, scalability, and maintainability.</p><p><br/></p><p>- Conduct research and stay updated on the latest AI trends, tools, and frameworks applicable to the retail domain.</p><p><br/></p><p>- Ensure ethical AI practices and compliance with data privacy regulations.</p><br/><p>Required Skills : </p><p><br/></p><p>- 2 to 4 years of experience in AI engineering, with a focus on retail applications.</p><p><br/></p><p>- Strong programming skills in Python and SQL.</p><p><br/></p><p>- Hands-on experience with ML/DL frameworks such as TensorFlow, PyTorch.</p><p><br/></p><p>- Experience working with LLMs and Generative AI models.</p><p><br/></p><p>- Proven track record of deploying models using cloud platforms - GCP preferred, also open to AWS and Azure.</p><p><br/></p><p>- Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, CI/CD).</p><p><br/></p><p>- Solid understanding of data structures, algorithms, and software engineering principles.</p><p><br/></p><p>- Experience with customer analytics, retail forecasting, and recommendation systems.</p><p><br/></p><p>- Knowledge of retail systems such as POS, ERP, CRM, and e-commerce platforms.</p><p><br/></p><p>- Exposure to Big Data technologies (e.g., Apache Spark).</p><br/><p></p><br/></p> (ref:hirist.tech)