Data Scientist: IV (Lead)

Hirekeyz Inc10 months ago
Columbus, OH, United States
On-site
Contract
Junior Level (1-3 years)

Job Description

Position Overview

Job Title: Data Scientist: IV (Lead). Location: Columbus Ohio - 43215 (On-site). Duration: 6 Months C2H.

Our Enterprise Data and Analytics team is growing. We’re looking for a Lead Data Scientist to assist with building and developing our Data Science team and lead us into the next generation of banking. We are reimagining how data is used across the bank to better serve our customers, support our communities, and make our colleagues' lives better. Our goal is to be the best performing Regional Bank in America, and we need data and analytics to meet that goal. As we advance our data science and analytics capabilities, we want a Lead Data Scientist to develop experts in modeling complex business problems and discovering business insights using statistical, algorithmic, mining, and visualization techniques. We are looking for a leader who has a passion for developing others, driving change, and continuously improving and evolving the application of technologies to meet today’s and tomorrow’s challenges.

Key Responsibilities

  • Prioritizes analytical projects based on business value and technological readiness.
  • Performs large-scale experimentation and builds data-driven models to answer business questions.
  • Conducts research on cutting-edge techniques and tools in machine learning, deep learning, and artificial intelligence.
  • Evangelizes best practices to analytics and products teams.
  • Acts as the go-to resource for machine learning across a range of business needs.
  • Owns the entire model development process—from identifying business requirements, data sourcing, model fitting, presenting results, to production scoring.
  • Provides leadership, coaching, and mentoring to team members, developing the team to work with all areas of the organization.
  • Works with stakeholders to ensure business needs are clearly understood and that services meet those needs.
  • Anticipates and analyzes trends in technology while assessing the impact of emerging technologies.
  • Coaches individuals through change and serves as a role model.

Required Qualifications

  • Up-to-date knowledge of machine learning and data analytics tools and techniques.
  • Strong knowledge in predictive modeling methodology.
  • Experienced at leveraging both structured and unstructured data sources.
  • Willingness and ability to learn new technologies on the job.
  • Demonstrated ability to communicate complex results to technical and non-technical audiences.
  • Strategic, intellectually curious thinker with a focus on outcomes.
  • Professional image with the ability to form relationships across functions.
  • Ability to train more junior analysts regarding day-to-day activities as necessary.
  • Proven ability to lead cross-functional teams.
  • Strong experience with Cloud Machine Learning technologies (e.g., AWS Sagemaker).
  • Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret).
  • Demonstrated expertise with at least one Data Science environment (R/RStudio, Python, SAS) and at least one database architecture (SQL, NoSQL).
  • Financial Services background preferred.
  • Master’s degree and 5+ years of relevant work experience using statistics and machine learning to solve complex business problems; experience conducting statistical analysis with advanced statistical software, scripting languages, and packages; experience with big data analysis tools and techniques; and experience building and deploying predictive models, web scraping, and scalable data pipelines.
  • Expert understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, and hierarchical/mixed models (multi-level modeling).
  • Master’s degree or PhD in computer science, statistics, economics, or related fields.

Required Skills

team leadership
scikit-learn
TensorFlow
non-linear regression
data analytics
data mining
predictive modeling
SQL
Bayesian Networks
AWS Sagemaker
NoSQL
Python
SAS
mentoring
model development
linear regression
R/RStudio
mixed models
machine learning
big data analysis
hierarchical models