Data Scientist

Overview

Founded in 1964, New York Blood Center Enterprises (NYBCe) has provided more than 60 years of lifesaving research, innovation, and impact. NYBCe is one of the largest nonprofit blood centers, spanning 17+ states and serving 75 million people. NYBCe operates Blood Bank of Delmarva, Community Blood Center of Kansas City, Connecticut Blood Center, Memorial Blood Centers, Nebraska Community Blood Bank, New Jersey Blood Services, New York Blood Center, and Rhode Island Blood Center, delivering one million blood products to 400+ U.S. hospitals annually. NYBCe additionally delivers cellular therapies, specialty pharmacy, and medical services to 200+ research, academic, and biopharmaceutical organizations. NYBCe’s Lindsley F. Kimball Research Institute is a leader in hematology and transfusion medicine research, dedicated to the study, prevention, treatment and cure of bloodborne and blood-related diseases. NYBC serves as a vital community lifeline dedicated to helping patients and advancing global public health. To learn more, visit nybc.org. Connect with us on Facebook, X, Instagram, and LinkedIn.

Responsibilities

The Data Scientist will work within the Enterprise Analytics and Data Science team to become an expert at understanding all NYBCe data domains and participate in delivering high-quality, high-velocity data products. This includes developing, training, and monitoring high-fidelity models and analyses to optimize products and processes as well as test the effectiveness of different courses of action.

 

Candidates must be able to report into one of the following NYBCe locations: New York City, NY; Kansas City, Missouri; St. Paul, Minnesota; Providence , RI and Newark, DE.

 

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Partner with the data engineering and analytics team to define measurement approaches, quantify and evaluate success, develop KPIs, and guide tracking efforts for ongoing measurement and reporting.
• Tackle complex and ambiguous analysis projects: provide clarity and leverage tools to answer complicated business questions.
• Use a variety of modeling techniques to increase and optimize donor, customer, and employee experiences, revenue generation, ad targeting, and other business outcomes.
• Perform detailed data/process analysis and create supporting visualizations and reports.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Ensure data quality and data hygiene across enterprise reporting platforms focusing on consistent, timely, and accurate data. Run regular tests and discrepancy checks to ensure continuity, accuracy, and quality of data.
• Develop reporting, visualizations, dashboards, and analytics products for all departments across the enterprise.
• Identify opportunities to improve reporting and analytics processes and identify opportunities to automate tasks.
• Any related duties as assigned.

Qualifications

Education:

• Bachelor’s Degree in Information Technology, Computer Science, or a related area.
• Master’s or Ph.D. in Statistics, Mathematics, Computer Science, or another quantitative field.

Experience:

• 4+ years of industry experience in data science, business analytics, statistics, business intelligence, or comparable data analysis role, including data warehousing and business intelligence tools, techniques, and technology.
• At least 4+ years of hands-on experience with Python and or R for statistical data analysis is required.
• Experience with Tableau, R/R-Shiny, or Python libraries (Matplotlib, Seaborn, ggplot…) to create impactful reports, visualizations, and interactive dashboards is required.
• 4+ years of experience manipulating data sets and building statistical models

Knowledge:

• Experience using statistical computer languages (R, Python, SLQ, etc.) to extract, manipulate and draw insights from large data sets.
• Knowledge in statistical and data mining techniques such as GLM/Regression, Random Forest, Boosting, Trees, text mining, and social network analysis.
• Experience analyzing data from 3rd party providers: Google Analytics, Facebook, etc.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, proper usage, etc.).

Skills: