Data Scientist - IT

Data Scientist (E&R)

Desired Competencies (Technical/Behavioral Competency)

Must-Have:

1. 8+ years of experience in a Data Science role

2. Working knowledge on traditional statistical model building( Example: Regression, Classification, Time series, Segmentation etc.), machine learning( Random forest, Boosting algorithms, SVM, KNN, etc.), deep learning(CNN,RNN,LSTM,Transfer learning) and NLP( Stemming, Lemitization,Named entity extraction,LDA, Latent semantic analysis etc).

3. Hands on experience in Python & R with a good proficiency level

4. Exposure in executing big data projects

5. Experience in building analytical/data science solution proposals

Good-to-Have

1. Bachelors or Masters in Statistics, Mathematics, Computer Science or another quantitative field

2. Strong problem-solving skills

3. Experience in E&R or Manufacturing domain

4. Prior experience in handling GIS data is a plus

5. Experience in executing Advanced Analytics projects using Big data and cloud environment

6. Demonstrate the ability to analyze and interpret data and translate it into meaningful solutions

7. Research and develop new analytic methodologies and approaches for leveraging data assets

8. Define & monitor KPI for measuring the efficiency of all modeling processes

9. Provide a consultative approach to assessing analytical approaches to business challenges

10. Continued research on latest advancements in statistical and mathematical modeling - identify opportunities to incorporate into projects to help support business initiatives

11. Knowledge of a variety of machine learning techniques & deep learning techniques and their real-world advantages/applications/drawbacks

12. A drive to learn and master new technologies and techniques

Responsibility of / Expectations from the Role

1. Work with stakeholders to provide data-driven solutions for stated business use cases or identify potential business use cases that bring value addition to stakeholder

2. Should be able to handle challenging business problems statements with research oriented and non-traditional approach

3. Exposure to a consulting mindset and big-picture thinking

4. Should have the willingness to learn and acquire domain knowledge as a continuous process

5. Coordinate with different functional teams to implement models and monitor outcomes.