Data Scientist

**Responsibilities**

**Strategic Roles and Responsibilities**
- Work as the data strategist with business stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Proactively look for unsolved, data-rich, high-impact business problems and create new opportunities in the AI solutioning pipeline
- Evaluate feedback and challenging issues gathered by the business and technical teams and transform them into tractable problems to resolve.
- Participate in research paper publishing, Applied AI Competitions; organize hackathons and knowledge conferences/webinars
- Work with Principal Data Scientist to devise solution roadmaps and KPIs.

**Operational Responsibilities**
- Design, develop and evaluate highly innovative DL/ML & Stats based models.
- Establish scalable, efficient, and automated processes for DL/ML model development, model validation and model implementation.
- Conduct feature engineering and implement DL/ML models into production by collaborating with software developers and machine learning engineers.
- Develop processes and tools to monitor and analyse model performance and data accuracy
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Aggregate data from multiple sources to provide a comprehensive assessment.
- Create reports, presentations, and process documents to display impactful results.
- Communicate solutions to stakeholders and implement improvements as needed to operational systems
- Recommend go / no-go on problem statements based on preliminary data analysis in consultation with Principal Data Scientists and AI Solution Managers

**Stakeholder Management**
- Collaborate with AI Champion and Business Data Analyst to define and ratify data requirements and sources
- Collaborate with AI Solution Managers, Principal Data Engineer and Principal ML Engineer to build ML/DL models and data prototypes

**Experience, Educational Qualification and Skills**

**Experience**:

- 5-8 years of relevant work experience

**Educational Qualification**:

- B.E/B.Tech and M.E./M.Tech (IT/Computer Science)
- Master’s in data science / Masters in Statistics/Economics

**Skills**:
**Mandatory**
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Deep understanding of applied statistical analysis and predictive modelling is desired such as regression, SVM, Tree, Random Forests, Boosting, Neural Network, clustering, forecasting, pattern analysis.
- Strong know-how of data exploration techniques, such as, mean-variance, k-means, nearest-neighbour, outlier techniques, and correlate anomalous sequences of events.
- Extensive know-how of Python /R and in object-oriented programming languages with high code quality
- Proficiency with data mining, applied mathematics, and statistical analysis

**Preferred**
- Skilled in the use of business intelligence tools, such as PowerBI, Tableau and frameworks like Hadoop, Hive etc.
- Working knowledge of software development languages
- Knowledge of Cloud Technologies are beneficial.
- Visualization skills and ability to prepare advanced graphical presentations and storytelling

**Behavioural**
- **Strategic Orientation (PL 2)**:

- **Entrepreneurial Mind-set (PL 1)**:

- **Stakeholder Partnering (PL 1)**:

- **Outcome Orientation (PL 2)**:

- **Transformation Mind-set (PL 1)**:

- **Innovation Focus (PL 3)**:

- **Team Orientation (PL 1)**:

- **Employee Development (PL 1)**
- Effective communication (written and oral) and data organization skills
- Logical and solution-oriented sharp thinking, high level of concentration and attention to detail

**People working in below domains (Preferred but not restricted to)**:
Energy Generation (Thermal, Renewable) Transmission Distribution, City Gas Distribution Sectors, Airport, Port & Logistics, Cyber Security, E-Commerce, FMCG, Natural Resource, Healthcare, Infrastructure, Défense