Data Scientist

Position level:

AI Specialist/Software engineering professional (Cant be a junior/fresher, needs to be middle to senior level person)

Work Experience:

Ideally, 4 to 5 years of working as a Data Scientist / Machine Learning and AI at a managerial position (end-to-end project responsibility). Slightly lower work experience can be considered based on the skill level of the candidate.

About the job:

Use AI-ML to work with data to predict process behaviors. Stay abreast of industry trends, emerging technologies, and best practices in data science, and provide recommendations for adopting innovative approaches within the product teams. In addition, championing a data-driven culture, promoting best practices, knowledge sharing, and collaborative problem-solving.

Abilities:

Knowledge about data analysis, Artificial Intelligence (AI), Machine Learning (ML), and preparation of test reports to show results of tests. Strong in communication with a collaborative attitude, not afraid to take responsibility and make decisions, open to new learning, and adapt. Experience with end-to-end process and used to make result presentation to customers.

Technical Requirements:

Experience working with real world messy data (time series, sensors, etc.)

Familiarity with Machine learning and statistical modelling

Ability to interpret model results in business context

Knowledge of Data preprocessing (feature engineering, outlier handling, etc.)

Soft Skill Requirements:

Analytical thinking Ability to connect results to business or process understanding

Communication skills Comfortable explaining complex topics to stakeholders

Structured problem solving – Able to define and execute a structured way to reach results

Autonomous working style – can drive a small project or parts of a project

Tool Knowledge:

Programming: Python (Common core libraries: pandas, numpy, scikit-learn, matplotlib, mlfow etc.); Knowledge of best practices (PEP8, code structure, testing, etc.)

Code versioning (GIT)

Data Handling: SQL; Understanding of data format (CSV, JSON, Parquet); Familiarity with time series data handling

Infrastructure: Basic Cloud technology knowledge (Azure (preferred), AWS, GCP); Basic Knowledge of MLOps workflow

Good to have: Knowledge of Azure ML, AWS SageMaker; Knowledge of MLOps best practices in any tool; Containerization and deployment (Docker, Kubernetes)

Languages: English – Proficient/Fluent

Location: Hybrid (WFO+WFH) + Availability to visit customer sites for meetings and work-related responsibilities as per the project requirement.