Sr Data Scientist  

KETOS provides users with real-time, actionable data and predictive insights on several water metrics (both volumetric and quality related) through remote, continuous unmanned smart monitoring for water grid management and water safety. 

KETOS is the one of the 1st in the Industry to integrate Water, Internet of Things (IoT), Full Stack Cloud Platform, AI/ML, Predictive Analytics and Data Science with a comprehensive patented solution of hardware devices (sensor nodes), secure IoT connectivity and an intelligent SaaS software platform for customer analytics while maintaining the reliability and data accuracy demanded in the Industry.

We are seeking an experienced Senior Data Scientist to participate in new product development from conception through deployment. You will have the opportunity to apply your knowledge of statistics and your analytical skills to mine data at scale an develop large-scale Machine Learning (ML) modules to reveal customer value data. You will support feature prototyping, and utilize industry best practices to write production-grade code. You will build data pipelines, implement ML-based analytical algorithms, and work closely with our software development team to set up back-end systems and interfaces that will deliver the next-generation analytics.


  • Development and implementation of data-intensive machine learning software for IoT, Water Analytics and Predictive Analytics.

  • Prototyping and validating advanced ML and Deep Learning (DL) models and algorithms that transform big data into actionable information

  • Providing data insight from massive amounts of data using data cleaning, data visualization, and statistical analysis tools and techniques

  • Setting up and maintaining databases supporting analytics research and feature prototyping

  • Writing production code to deliver analytics feature content as an Internet-of-Things (IoT) solution

  • Ensuring excellence in delivery to internal and external customers

  • Collaborating with data and subject matter experts from KETOS and its customer teams to seek, understand, validate, interpret, and correctly use new data elements.


  • MS or PhD in Computer Science, Electrical Engineering, Statistics, or equivalent fields with 5+ years hands-on industry work experience as Data Scientist or Senior Machine Learning Engineer

  • Strong understanding of machine learning algorithms & principles (regression analysis, time series, probabilistic models, supervised classification and unsupervised learning), and their application.

  • Expert in data mining, machine learning, deep learning, statistical modeling and data visualization techniques using data-oriented tools and languages such Python or R with data analysis libraries (pandas, sklearn, numpy, scipy, dash and matplotlib)

  • 5+ years experience writing SQL-like as well as NoSql queries and databases

  • 3+ years hands-on industry work experience designing and building large-scale data, machine learning, and analytics applications and pipelines that are well-designed, cleanly coded, well-documented, operationally stable, and timely delivered

  • Applied Machine Learning experience (regression analysis, time series, probabilistic models, supervised classification and unsupervised learning). 

  • Strong mathematical background (linear algebra, calculus, probability and statistics).

  • Experience with scalable ML (MapReduce, streaming).

  • Experience with deep learning algorithms and techniques, including but not limited to: CNN, LSTM, RNN, TensorFlow, Keras, Caffe, PyTorch

  • Experience setting up and using large-scale distributed data-processing frameworks such as Apache Spark and Hadoop Map-Reduce

  • Experience working with enterprise-grade cloud computing platforms such as AWS, Azure or GCP

  • Demonstrated ability to develop high-quality code adhering to industry best practices (i.e., code review, unit tests, revision control)

  • Work/project history reflective of a self-motivated professional who excels when given open-ended problems and broadly-defined goals, having an innate desire to discover the patterns and relationships in data that can be leveraged to provide business value