Job Description

Locations

• India, Gurugram

Skills Required

• at least one programming language central to AIintermediate
• AI automation platformsintermediate
• at least two statistical techniquesintermediate
• variety of visualizationsintermediate

Required Qualifications

• Bachelor's Degree in data science, computer science, mathematics, statistics, or related field with applicable data science work experience. And Master's Degree or PhD in Data Science or quantitative discipline preferred. (experience)
• Experience- 7 years - 10 years (experience, 7 years)
• Expert proficiency in at least one programming language central to AI and Data Science (e.g. Python). (experience)
• Familiarity with AI automation platforms and cloud-based native tools such as data storage, distributed computing, business intelligence, and infrastructure as code (Power BI, Azure, Fabric etc.). (experience)
• Bachelor's Degree in data science, computer science, mathematics, statistics, or related field with applicable data science work experience. And Master's Degree or PhD in Data Science or quantitative discipline preferred. (experience)
• Experience- 7 years - 10 years (experience, 7 years)
• Expert proficiency in at least one programming language central to AI and Data Science (e.g. Python). (experience)
• Familiarity with AI automation platforms and cloud-based native tools such as data storage, distributed computing, business intelligence, and infrastructure as code (Power BI, Azure, Fabric etc.). (experience)

Preferred Qualifications

• Proficient in at least two statistical techniques (e.g. time series modelling, classification, computer vision); in identifying the most appropriate statistical tool for a particular use case and project. (experience)
• Familiarity with a variety of visualizations (e.g. correlation heat maps, bubble charts) and demonstrate financial acumen to develop financial impacts for existing projects and evaluating new opportunities (experience)
• Nice to have: Microsoft Azure or Fabric data science/ AI related certifications here. (certification)
• Proficient in at least two statistical techniques (e.g. time series modelling, classification, computer vision); in identifying the most appropriate statistical tool for a particular use case and project. (experience)
• Familiarity with a variety of visualizations (e.g. correlation heat maps, bubble charts) and demonstrate financial acumen to develop financial impacts for existing projects and evaluating new opportunities (experience)
• Nice to have: Microsoft Azure or Fabric data science/ AI related certifications here. (certification)

Responsibilities

• Translate stakeholder requirements to potential AI & data science solutions independently.
• Contribute to discussions on identifying opportunities where machine learning can be applied to real-world business opportunities with key stakeholders.
• Support data science and AI collaboration efforts with partners across functions and teams
• Build and deliver presentations and communications that explain critical functions and findings to stakeholders with some guidance.
• Apply fundamental and some advanced concepts, practices and procedures for problem solving
• Automate repetitive data prep, feature engineering, and model monitoring pipelines with AI-driven tools.
• Explore and integrate large language models (LLMs) and generative AI solutions to accelerate exploratory data analysis, reporting, and knowledge discovery.
• Partner with engineering teams to operationalize models, embedding automation into core products and processes.
• Continuously evaluate and improve model performance using automated monitoring, retraining, and alerting systems
• Ability to manage individual tasks with minimal guidance and flag project risks as they are identified.
• Translate stakeholder requirements to potential AI & data science solutions independently.
• Contribute to discussions on identifying opportunities where machine learning can be applied to real-world business opportunities with key stakeholders.
• Support data science and AI collaboration efforts with partners across functions and teams
• Build and deliver presentations and communications that explain critical functions and findings to stakeholders with some guidance.
• Apply fundamental and some advanced concepts, practices and procedures for problem solving
• Automate repetitive data prep, feature engineering, and model monitoring pipelines with AI-driven tools.
• Explore and integrate large language models (LLMs) and generative AI solutions to accelerate exploratory data analysis, reporting, and knowledge discovery.
• Partner with engineering teams to operationalize models, embedding automation into core products and processes.
• Continuously evaluate and improve model performance using automated monitoring, retraining, and alerting systems
• Ability to manage individual tasks with minimal guidance and flag project risks as they are identified.

Tags & Categories