Please make sure candidate should qualify checklist written below - 

Ques: What is the difference between Bagging and boosting? Which is used when? (Needs to be filled by the candidate )

Ques: Is the candidate willing to work as an Individual Contributor?

Ques: Does the candidate has atleast 7-8 years of rich experience in Data science?

Ques: If I want to use regularization method for feature selection, which regularization should I use and why? (Needs to be filled by the candidate )

Ques: What problems autocorrelation / heteroskedasticity cause in a linear regression model? (Needs to be filled by the candidate )

Ques: Is the candidate a Full time MSC in Statistics, Mathematics, PhD in Operations Research?

Ques: Does the candidate has rich experience in Statistical modeling, Text mining( most important) scoring models,response models,optimization,forecasting,segmentation, and machine learning? If yes please specify

DESIRED QUALIFICATIONS

1. Bachelor- s/Master's Degree in Engineering/Statistics or PhD in Data Science, Operations Research, Machine Learning, Applied Statistics, Data Mining and/or related quantitative engineering field.

2. 8-15 years of experience in building and implementing statistical models.

RESPONSIBILITIES

1. Works independently or with a small team to solve complex problems and create scalable models/algorithms that will be integrated into companies tools and products.

2. Works directly with product managers & senior leadership to translate their vision into workable algorithms.

3. Will focus on creating output that is simple and easy for business users to understand and implement.

4. Leads junior data scientists (if required) to deliver results.

5. Will communicate progress of projects from time to time to various stakeholders.

6. Participates in industry forums to showcase the analytical depth of the organization.

DESIRED EXPERIENCE

1. Prior experience in building and rolling out scoring models, response models, optimization, forecasting, segmentation etc.

2. Strong understanding of statistical modeling and its application to solving business problems.

3. Hands-on experience with python for building models, Clementine, IBM DataMiner,Oracle Data miner etc. is a must.

4. Expertise in management of large data and understanding of data quality metrics

5. Hands-on experience with Python, big data technologies, tensor flow and other machine learning platforms.

6. Ability to collaborate with various teams to learn from their experience and apply the learning's to the problems at hand.

7. Great communication skills with the ability to clearly communicate data, context, and implications to business stakeholders and senior leaders.

This job opening was posted long time back. It may not be active. Nor was it removed by the recruiter. Please use your discretion.