The application of DS/ML in customer engagement like ours is a green field with many new problems to solve. Some challenging problems in our IT-BU include analysis of time series data to look for anomalous behavior, predictive modelling of issues in the IT infrastructure, correlating logs, metrics, traces and other events to automatically and quickly identify root causes of the problems.
These problems will allow you to work on large data sets -- hundreds of thousands of streaming metrics, millions of log lines, thousands of events would have to be analyzed in real time to make predictions, and provide suggestions. Imagine the next generation of IT services being offered not
via dashboards but in an interactive, conversational manner, backed by real time analysis of the entire streaming data into an operating center. This vision is what you would make into a reality. You will build algorithms that analyze these millions of streaming data points, build a distributed, scalable, and high performance system to serve the insights to the customer in a conversational UI manner.
Responsibilities:
- Collaborate with product and business teams to understand all aspects of the problem
- Define the right target metrics that best represent the end-user value
- Apply knowledge of ML, statistics, and advanced mathematics to conceptualize, experiment and design an intelligent system
- Build efficient systems for processing large amounts of data; be proficient with distributed programming frameworks such as Hadoop/Spark
- Work closely with ML Engineers to come up with scalable system and model architectures for enabling real-time ML/AI services
- Build Own ML pipelines end-to-end, including stages such as data pre-processing, model generation, cross-validation, and share feedback
Qualifications
- A Bachelor s degree or a higher degree in Computer Science, Statistics, Mathematics, or a related field.
- Strong problem-solving and programming skills
- Solid understanding of mathematical underpinnings behind Machine Learning algorithms and proficiency in probability, statistics, linear algebra, calculus, and optimization.
- Must have experience in ML with a proven record of successful ML projects with strong individual contribution
- Experience with time series analysis, NLP, Distributed Systems, large scale computing, Big Data technologies like Hadoop and Spark are plus.
- Solid background in at least two of the following areas: Time series analysis, statistical ML techniques, graph algorithms, constraint optimization, natural language processing, distributed systems, and deep learning. Natural language processing, statistical ML techniques, graph algorithms, constraint optimization, signal processing (speech or vision), deep learning, distributed systems
- Proficiency with Database systems, schema design (SQL and noSQL)