Machine Learning Consultant, Public Sector AWS Professional Services - India

Key Skills

HTML, JavaScript, Java Programming, Design of Experiment, Python Programming, Database, SQL, AWS, Machine learning techniques, Perl Programming, MATLAB Programming, NoSQL, Bash scripting, ETL frameworks

Job Description

Job summary

Excited by using massive amounts of data to develop Machine Learning (ML) and Deep Learning (DL) models? Want to help public sector customers 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? As a Data Machine Learning Consultant in AWS ProServe India LLP (‘ProServe India’), we are helping internal customers build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. 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 WWPS Professional Service office. We’re looking for top architects, system and software engineers capable of using ML and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Key job responsibilities

• Design data architectures and data lakes
• Provide expertise in the development of ETL solutions on AWS
• Use ML tools, such as Amazon SageMaker Ground Truth (GT) to annotate data. Work with Professional Services on designing workflow and user interface for GT annotation.
• Collaborate with our data scientists to create scalable ML solutions for business problems
• Interact with internal customer to understand the business problem, help and aid them in implementation of their ML ecosystem
• Analyze and extract relevant information from large amounts of historical data — provide hands-on data wrangling expertise
• Work closely with account team, research scientist teams and product engineering teams to drive model implementations and new algorithms

About the team

Public Sector Professional Services (AWS Professional Services) lead Government, education, non-profits, and healthcare organizations; and their IT providers through their cloud adoption journey and help them achieve their desired business outcomes through AWS leadership and technical expertise. AWS Professional Services optimize the customers' model, reduce risk, and shorten customers' time to value via scientific breakthroughs, creating a broader reach to constituents, and producing productivity benefits, allowing for them to focus on their core missions.

BASIC QUALIFICATIONS

• Highly technical and analytical, possessing 5 or more years of IT platform implementation experience (data analytics, data warehousing concepts and techniques including extensive knowledge and use of star schema, machine learning)

PREFERRED QUALIFICATIONS

• Scripting language (bash, Perl, Python) - Proven ability in managing databases of 1-10TB in size
• Understanding of OLTP vs OLAP data administration needs - Experience in ETL/ELT workflow management - Understanding and ability to participate in all phases of the SDLC including requirements gathering, business analysis, configuration management and quality control
• Working knowledge of deep learning, machine learning and statistics.
• User interface experience with Javascript, HTML
• Knowledge of ETL tools and databases (both SQL-based, NoSQL)
• Experience in using Python, R or Matlab or other statistical/machine learning software
• Strong communication and data presentation skills
• The motivation to achieve results in a fast-paced environment.
• Comfortable working in a fast paced, highly collaborative, dynamic work environment