Senior Data Analytics Engineer

Job Description

Overview

At Zebra, we’re reinventing how businesses operate at the enterprise edge - helping them run faster, smarter, and more connected than ever before.

A dynamic community of builders, doers and problem solvers, we each play a unique role - shaping new technologies, bringing solutions to market, and partnering with companies on the front line of business.

Being a part of Zebra means making your mark as we make digital transformation a reality.

It means growing into a leader at a leading company that makes a distinct difference - because together, we’ve only just begun.

We build today, so we can create tomorrow.

Join Zebra and change the world with us.

The Senior Data Analytics Engineer designs, develops applications using diverse data, which can be inclusive of machine learning models (Clustering, Classification or Regressions models), Artificial/Augmented Intelligence systems. This Engineer is capable of applying any of the following data engineering methods: feature engineering, statistical summary analysis, ML modelling and AI techniques, on large structured, unstructured, diverse “big data” sources of machine-generated unbounded data to generate impactful insights and foresights for real-life business problem solutions and product features development and improvements. Be a member of a close-knit team of data analysts, data scientists, and software developers analyzing large, multidimensional datasets from internal and external sources, both onPrem and in Cloud environments. Looking for this person to be a bold self-starter who can apply novel models and engineering solutions on customer data in order to help identify areas to increase revenue and solution margins.

Responsibilities

• Work closely with business partners to identify low-cost, efficient data solutions.
• Designs and implementation of regression based predictive models incorporating diverse data types.
• Partners with experimentalists to translate models into testable hypotheses.
• Become familiar with Data domain of Software Solutions in Zebra.
• Experience or ability to become a sole contributor to tools to support analysis and visualization of large datasets.
• Develops, codes software programs, implements industry standard auto ML models (Speech, Computer vision, Text Data), Statistical models, relevant ML models (devices/machine acquired data), AI models and algorithms.
• Identifies significant insights based on predictive ML models from large data and metadata sources; interprets and communicates foresights, insights and findings from experiments to product managers, service managers, business partners and business managers.
• Manages data quality testing End-to-End on Data Pipelines and Software Solutions, by using multiple applications to test in databases, flat files, and cloud data stores (SQL & NoSQL).
• Makes use of Rapid Development Tools (Business Analytics Tools, Graphics Libraries, Data modeling tools) to optimally communicate research findings using visualization, machine learning model features, feature engineering / transformations to relevant partners.
• Analyze, review and track trends and tools in Data Science, Machine Learning, Augmented Intelligence, Artificial Intelligence and IoT space to help in making build/buy/partner recommendations for Enterprise Software Business Unit of Zebra.
• Interacts with multi-functional teams to identify questions and issues for data engineering pipelines, machine learning models feature engineering.
• Manages and indirectly leads Data Scientist peers
• Drives innovation by encouraging open, high energy environment; lead participation in innovation summit and expos, recommend relevant training and conference for employees to attend, publish papers and patent disclosures.
• Meets with customers, partners, product managers and business leaders to present findings, predictions, foresights; collects customer specific requirements of business problems/processes; Identify data collection constraints and alternatives for implementation of models.
• Helps efficiently maintain cloud Infrastructure for research, staging and beta/pilot environment (from costs perspective for working with huge data sets)

Qualifications

• Bachelor’s in Computer Science, Machine Learning, or related subject area. Master’s is preferred
• Strong background in statistics, machine learning, deep learning and programming necessary. 5+years experience required
• 3+ years of experience of scripting with languages like Java, Python or Scala for data processing is preferred.
• Strong experience in large structured and unstructured datasets, from both batch and unbounded data sources, feeding enterprise workflow and transactional datastores is preferred.
• Strong experience working with unstructured and ‘messy’ files of different formats (.txt. AVRO, .csv, .json), and finding anomalies or methods for normalizing these structures for accurate storage/insert into data store is preferred.
• Prior experience in Natural Language Processing using NLTK and with Tensorflow is preferred.
• Retail industry experience desired.
• Experience using cloud compute (e.g. Google Cloud Platform, AWS, Azure).
• Work with a streaming service like Kafka is a plus.
• Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark)
• Proven understanding of data structures, software design and architecture