Data Scientist

A Bachelor or Masters Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience - 6+ years of industry experience in predictive modeling, data science and analysis - Previous experience in a ML or data scientist role and a track record of building ML or DL models - Experience using Python and/or R - Knowledge of SparkML - Able to write production level code, which is well-written and explainable - Experience using ML libraries, such as scikit-learn, caret, mlr, mllib - Experience working with GPUs to develop models - Experience handling terabyte size datasets - Experience diving into data to discover hidden patterns - Familiarity with using data visualization tools - Knowledge and experience of writing and tuning SQL - Past and current experience writing and speaking about complex technical concepts to broad audiences in a simplified format - Experience giving data presentations

Job summary

Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help the largest global enterprises derive business value through the adoption of Artificial Intelligence (AI)? Eager to learn from many different enterprise’s use cases of AWS ML and DL? Thrilled to be key part of Amazon, who has been investing in Machine Learning for decades, pioneering and shaping the world’s AI technology?

AWS Professional Services is a unique consulting team in ProServe India. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML or DL models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers.

If you do not live in a market where we have an open Data Scientist position, please feel free to apply. Our Data Scientists can live in any location where we have a Professional Service office.
- Understand the internal customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances.
- Assist internal customers by being able to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.
- Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our internal customers build DL models.
- Use SparkML and Amazon Machine Learning (AML) to help our internal customers build ML models.
- Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data.
- Work with our Professional Services DevOps consultants to help our internal customers operationalize models after they are built.
- Assist internal customers with identifying model drift and retraining models.
- Research and implement novel ML and DL approaches, including using FPGA.

This role is open for Mumbai/Pune/Bangalore/Chennai/Hyderabad/Delhi
- PhD in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.)
- 6+ years of industry experience in predictive modeling and analysis
- Skills with programming languages, such as Java or C/C++
- Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
- Consulting experience and track record of helping customers with their AI needs
- Publications or presentation in recognized Machine Learning, Deep Learning and Data Mining journals/conferences
- Experience with AWS technologies like Redshift, S3, EC2, Data Pipeline, & EMR
- Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer’s organization
- Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment