Position - Data Scientist
Location Branchburg NJ
Long Term
We are looking for a Data Scientist to join our network data analytics experts' team to add value to our current data processes. The ideal candidate will demonstrate independent working skills as well as proficiency in collaboration to ensure our functions which include data collection, cleaning, and preprocessing to build and implement machine learning and/or artificial/alternative intelligence algorithms to assist the Network Repair Bureau with its needs of proactively solutioning customer's issues, helping both to identify those issues as well as solve them.
Responsibilities
Work with partner teams to identify relevant data and better understand the Network Repair Bureau and Verizon Wireless customer and network insights.
Act as an idea leader within the business team to drive insight-based decision-making and growth of data science methodology.
Identify, design, and implement internal process improvement, automate manual processes, optimize data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources using SQL and Spark 'big data' technologies and leveraging the Google Cloud Platform.
Perform ad-hoc analysis and develop reproducible processes to meet business requirements.
Verifying data quality and/or ensuring it via data cleaning.
Exploring data to ensure a correct understanding of the information contained, working with Subject Matter Experts to confirm and/or enrich the understanding, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
Understanding business objectives, developing models that help achieve them and creating and tracking metrics to reflect their development.
Managing available resources such as hardware, data, and personnel to meet deadlines.
Designing, developing, and researching Machine Learning systems, models, and schemes.
Studying, transforming, and converting data science prototypes into fully scalable models.
Performing statistical analysis and using results to improve models.
Training and retraining ML systems and models to accurately and quickly reflect the network in real-time.
Analyzing the use cases driving the need for machine learning algorithms and ranking them by their success probability to identify which models are 'best fit.'
Understanding when your findings can be applied to business decisions and be prepared to make a case and defend the model's applicability to those business use cases.
Enriching current processes with machine learning and/or artificial/alternative intelligence models and prepared to explain the models to those less informed about Data Science practices.
Additional Responsibilities:
Support the various AI/ML models that need to be deployed to production
Troubleshoot various activities and rectify any problems that arise in the end-to-end model pipeline
Test and validate AI Models. Deploy and manage models throughout the end-to-end lifecycle.
Industrialize the model pipeline which includes Testing & validation of the model pipeline
Be part of the requirements for new enhancements in model/solution
Propose new ideas/solutions that can be helpful for the project overall
Analyze and understand problems and issues to convert these insights into system requirements
Build scalable and high-performance Machine Learning and Data Mining algorithms
Skills:
Proficiency with a deep learning framework such as TensorFlow or Keras
Proficiency with Python and basic libraries for machine learning such as scikit-learn and pandas
Knowledge of data warehouse and data lake technologies (Teradata, Hadoop, No SQL, graph DB, GCP, etc.)
Expertise in visualizing and manipulating big datasets
Familiarity with Linux, Spark, and Kafka
Understanding how to present and share data with management, including using Microsoft Powerpoint and/or Google Slides.
Experience developing and delivering end-to-end data insights, including data ETL, partnership with relevant Subject Matter Expert (SME) teams to ensure the validity of ETL process, derive insights, and share.