Data Scientist, Professional Senior

Zebra Technologies Corporation•Lincolnshire, IL

8d•Hybrid

About The Position

Designs, develops, programs and implements Machine Learning Models (Clustering, Classification or Regressions models), Implements Artificial/Augmented Intelligence systems, Performs Statistical Modelling and Measurements by applying data engineering, feature engineering, statistical methods, ML modelling and AI techniques on structured, unstructured, diverse “big data” sources of machine acquire data to generate actionable insights and foresights for real life business problem solutions and product features development and enhancements. The Data Scientist Senior will 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. The Data Scientist Senior will develop and apply novel models on customer data in order to help identify areas to increase profits and reduce losses.

Requirements

Bachelor’s in Computer Science, Machine learning, or related discipline.
5+ years experience required.
Strong background in statistics, machine learning, deep learning and programming necessary.
Hands-on experience with the Azure cloud platform, particularly Azure Databricks.
Proficient in large-scale data processing using PySpark and querying with Databricks SQL.
Demonstrable experience building and deploying end-to-end ML solutions.
Familiarity with CI/CD principles and tools like GitHub Actions, and experience with automated deployment frameworks such as Databricks Asset Bundles.

Nice To Haves

Master’s preferred.
Experience in solving large-scale real-world industry problems, preferably in collaboration with cross-functional, multidisciplinary teams.
Knowledge of statistical programming techniques and languages (e.g., R, Python, Java, etc.).
Working knowledge of common machine learning and deep learning approaches (e.g. regression, clustering, classification, dimensionality reduction, supervised and unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning) and data analysis packages (e.g. scikit-learn, pyclustering, pathways analysis, MLlib).
Prior experience with Tensorflow.
Prior experience in Natural Language Processing using NLTK.
Retail industry experience desired.
Experience using cloud compute (e.g. Google Cloud Platform, AWS, Azure).
Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark).
Solid understanding of data structures, software design and architecture.
Ability to work independently and take initiative, but also a cooperative team player.

Responsibilities

Designs and implementation of Regression based predictive models incorporating diverse data types.
Integrates state-of-the-art machine learning algorithms as well as the development of new methods.
Partners with experimentalists to translate models into testable hypotheses.
Develops 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 meaningful foresights 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.
Makes use of Rapid Development Tools (Business Intelligence Tools, Graphics Libraries, Data modelling tools) to effectively communicate research findings using visual graphics, Data Models, machine learning model features, feature engineering / transformations to relevant stakeholders
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
Interacts with Cross-Functional teams to identify questions and issues for data engineering, machine learning models feature engineering.
Evaluates and makes recommendations to evolve data collection mechanism for Data capture to improve efficacy of machine learning models prediction.
Helps efficiently maintain cloud Infrastructure for research, staging and beta/pilot environment (from costs perspective for working with huge data sets)
Mentors, guides, and indirectly leads less experienced Data Scientist peers
Drives innovation by fostering open, high energy, collaborative 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; Gather customer specific requirements of business problems/processes; Identify data collection constraints and alternatives for implementation of models.