Data Scientist

2 Locationsmid

Data ScientistData

1 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

Querying, pre-processing, data cleaning,

Technical Tools

Data ScientistData

What We Offer

~3 min read

Be part of an organization that’s driving change and consistently recognized as a top employer. At Deluxe, we know that great people build great companies—and we invest in you accordingly.

✓We’re proud to be recognized as a Great Place to Work and a top workplace for Moms, Dads, LGBTQ+ employees, and Veterans.

✓We offer competitive benefits starting on day one, designed to support your life both in and out of work.

✓42% of our employees have stayed for 10+ years, citing our people, benefits, work-life balance, inclusive culture, and team support as key reasons why.

✓Healthcare (Medical, Dental, Vision)

✓Paid Time Off, Volunteer Time Off, and Holidays

✓Employer-Matched Retirement Plan

✓Employee Stock Purchase Plan

✓Short-Term and Long-Term Disability

✓Infertility Treatment, Adoption and Surrogacy Assistance

✓Tuition Reimbursement

Responsibilities

~1 min read

→Querying, pre-processing, data cleaning, feature engineering and analyzing large amounts of structured and Unstructured data(terabytes/petabytes) across multiple data sources using structured query language(SQL), Python, Pytorch, Pyspark, R, Spark and Scala. In a Cloud Native AWS environment.
→
Deliver custom and commercial scalable solutions for internal and external customers. Combine business requirements and existing processes and data knowledge to create analytical solutions by building and deploying unsupervised and supervised machine learning and deep learning models. Including combining models using ensemble modeling techniques. Required to have knowledge and experience in unsupervised learning, principle component analysis(PCA), GLM, lasso/ridge regression, random forest, gradient boosted machines(GBM’s), XGBoost, Baysian optimization, natural language processing(NLP) and deep neural networks/ back propagation.
→
Use advance statistical concepts for sampling, descriptive statistics, hypothesis testing, data quality, performance testing, attribution analysis, multi variate segmentation and recommender systems.
→
Collaborate with other data scientists to solve demanding and complicated business problems by applying machine learning, deep learning to large data sets. Partner with Data Engineering on product development(proof of concept to commercial product), SDLC and CI/CD pipelines to process data, train models, test predictions within a MLOPS framework all at scale and be able to provide requirements for deployment. Must be able to work within an agile framework.

Requirements

~1 min read

Education and Experience: Bachelor’s degree in Computer Science, Math, Statistics, Machine Learning, Analytic, Data Science and 0 to 2 years experience.

Must be 18 years of age or older

Education: Masters Degree in Computer Science or Statistics

Experience: 1 year in Data Science