The Data Science Lead will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.
Responsibilities
- Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
- Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
- Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
- Use big data technologies to help process data and build scaled data pipelines (batch to realtime)
- Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
- Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
- Automate ML models deployments
Requirements
6 – 10 years of overall experience that includes at least 4+ years of hands-on work experience, data science / Machine learning
Minimum 4+ year of SQL experience
Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred
Skills, Abilities, Knowledge
- Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models. Knowledge of Demand Forecast models is a plus
- Programming Skills – Hands-on experience in statistical programming languages like Python, R and database query languages like SQL
- Cloud (Azure) – Experience in Databricks and ADF
- Model deployment experience will be a plus
- Experience with version control systems like GitHub and CI/CD tools
- Experience is Exploratory data Analysis
- Knowledge of ML Ops / DevOps and deploying ML models is required
- Experience using MLFlow, Kubeflow etc. will be preferred
- Experience executing and contributing to ML OPS automation infrastructure is good to have
- Exceptional analytical and problem-solving skills
- Experience building statistical models in the Commercial, Net revenue Management
Nice to have
- Familiarity with Spark, Hive, Pig is an added advantage
- Model deployment experience will be a plus
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.
