- Perform requirements analysis of assigned projects to ensure correct mapping of requirements to solution design
- Perform data science solution development including:
- Performing exploratory data analysis,
- Performing statistical data modeling,
- Building descriptive/predictive/prescriptive models,
- Performing experimentation with testing & validation,
- Performing model optimization
- Deliver high quality solutions to meet project requirements using state-of-the-art DA/DS/ML/AI techniques
- Implement solution using agile sprints
- Collaborate with business and technical stakeholders to deliver projects
- Implement appropriate visualization artefacts to demonstrate solution performance for project stakeholders
- Communicate project outcomes with project stakeholders through presentations & reports
Skills & Experience:
- Knowledge about industrial or machine analytics is required. Must have domain experience in some industrial sectors such as – Oil & Gas, Energy, Utilities, Automotive etc.
- Hands-on experience in at least some of the following processes or related areas in the context of analytics and associated challenges is required:
- Predictive maintenance
- Risk analysis
- Condition monitoring
- Reliability and failure modeling
- Process modeling
- Any experience in the area of discovery and analytics involving any of the following would be an advantage:
- Image, Video analysis
- Spectral data analysis (hyper spectral image analysis etc.)
- Signal data processing (Infrared, imagery etc.)
- Hands on experience in ML/AI/analytics implementation
- Industry Analytics/ML/AI project delivery experience using Agile methods
- Experience in building production DS solutions including API deployment using Cloud
- Working directly with business stakeholders, data engineers, solutions architects and product managers in creating robust solutions.
- Current knowledge about ML algorithms, libraries and frameworks (should be demonstrable via coding test in Python etc. languages)
- Effective verbal and written communication
Education & Qualifications:
- PhD or Masters in Computer Science and/or an industrial discipline.
- Demonstrated continuous learning in ML / Data Science through formal training/certification
- ML Ops/ AI Ops related formal qualification and/or equivalent experience is a plus.
Desired Skills:
- ML Ops/Al Ops
- Python
- ML algorithms
- DA/DS/ML/AI techniques
- Spectral data analysis
- Signal data processing
Desired Qualification Level:
- Masters