ML Architect

Essential functions

• Lead the architecture, development, and deployment of optimization and machine learning models with a focus on price optimization, leveraging frameworks and solvers like OR-Tools, Pyomo, Gurobi or SCIP.

• Design and implement end-to-end pipelines, handling large datasets to extract insights and apply optimization techniques in pricing strategies.

• Collaborate with Data Scientists, Engineers, and business stakeholders to ensure the alignment of pricing strategies with broader organizational goals.

• Leverage advanced optimization algorithms (e.g., MIP, LP, CP-SAT) and techniques such as heuristics to drive pricing decisions with many combinatorial outcomes.

• It is good to have knowledge of Statistical and ML techniques in demand forecasting, price elasticity, and competitive pricing analysis to ensure robust pricing decisions.

• Conduct experiments and refine models to continuously improve the accuracy and plans of price predictions and promotional strategies.

• Work on cloud platforms such as Google Cloud, AWS, and Azure to build scalable, production-level ML architectures.

• Mentor junior engineers and data scientists, providing guidance on technical challenges and fostering a culture of continuous improvement.

• Lead and participate in sprint planning, technical project planning, and coordination of pricing projects across multiple teams.

• Maintain high standards of model interpretability and ensure the integration of models with business processes, optimizing key KPIs such as revenue, customer acquisition, and retention.

Qualifications

• Advanced Degree: Ph.D. or Master’s in Computer Science, Data Science, Applied Mathematics, or a related field.

• Experience: 5+ years of experience in machine learning/operational research and data science, with at least 3 years focused on price optimization, demand forecasting, or promotional strategies.

• Programming Languages: Proficiency in Python with extensive experience using ML and Operational research libraries such as PyTorch, Scikit-learn, Pyomo, OrTools, Gurobi, ElasticSearch / OpenSearch, TypeScript

• Frameworks & Tools: Deep knowledge of ML frameworks and data processing tools like Apache Spark, Pandas, Numpy, SciPy, and Matplotlib.

• Cloud Platforms: Hands-on experience with cloud environments such as Google Cloud, AWS for ML deployment and scaling.

• Optimization Algorithms: In-depth understanding of constrained optimization algorithms LP, MIP and/or CP-SAT.

• Price Optimization Expertise: Proven experience in building and optimization solution, such as pricing models

• Experience with Deep Learning (Computer Vision, NLP)

• Experience with LLMs

Would be a plus

• Generative AI Expertise: Knowledge of cutting-edge AI technologies, such as Generative AI for innovative pricing solutions.

• AI Strategy: Experience working with senior business stakeholders to align AI strategies with business goals, defining MVPs, and scaling AI projects.

• Cross-functional Collaboration: Experience collaborating with engineering, R&D, and business teams to drive large-scale pricing initiatives in retail, manufacturing, or similar industries.

• Publication & Conference Speaker: Track record of contributing to research, publishing papers, or speaking at ML conferences.

• Software engineering skills: It is good to have experience with building micro services and tools such as FastAPI, SQLAlchemy, Pydantic, Docker etc.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.