Job DescriptionMachine Learning Engineer
As an MLE, you will apply strong expertise in Artificial Intelligence through the use of ML, data mining, & data retrieval to design, prototype, & build next generation advanced analytics engines & services. You’ll collaborate with cross-functional teams & business partners to define the tech problem statement & hypotheses to test. You will develop efficient & accurate analytical models which mimic business decisions & incorporate those models into analytical data products & tools. You will have the opportunity to drive current & future strategy by leveraging your analytical skills as you ensure business value & communicate the results.
Collaborate with business partners to develop innovative solutions to meet objectives utilizing cutting edge techniques & tools
Create repeatable, interpretable, dynamic & scalable models that are seamlessly incorporated into analytic data products
Reviewing .py files to decipher business logic & process flow
Design & optimize scalable ML solutions
Build production ready ML platforms
Provide production support & troubleshooting
Provide technical leadership & influence data-driven optimization efforts
Engineer features by using your business acumen to find new ways to combine disparate internal & external data sources
Effectively communicate the analytics approach & how it will meet & address objectives to business partners
Bachelors Degree in Data Science, Computer Science, Engineering, Statistics or related disciplines & 5+ years of experience working on Data Science & Data Engineering projects
Strong engineering skills in Python, Spark, Pyspark, SQL; ability to decipher complex Pyspark codes
Experience with Relational databases with multidimensional structures
Experience with large interconnected code repositories, & working on projects spread across multiple .py documents
Ability to write production code in Python & Scala; writing complex SQL queries & ML specific production codes on Sklearn, Spark ml, Tensorflow etc.
Hands-on experience in Hive & PySpark & open source tools such as kafka, BigQuery etc.
Great command on ML concepts & algorithms which works on the big data world. Ability to implement ML best practices for the entire Data Science lifecycle
Proficiency with ML concepts & modeling techniques to solve problems such as clustering, classification, regression, anomaly detection, simulation & optimization problems on large scale data sets.
Extensive data modeling & data architecture skills