Senior Data Scientist

National Debt Relief, LLC.•,

27d•$149,000 - $171,500•Remote

About The Position

National Debt Relief (NDR) is seeking an experienced and versatile Senior Data Scientist to join our Data Intelligence team and round out our capabilities. This role is designed for an experienced data science professional with proven experience in building and deploying enterprise-grade production machine learning (ML) and artificial intelligence (AI) models. As a key contributor, you will augment the team's existing strengths in classification and regression analysis by bringing advanced expertise in techniques such as unsupervised learning, clustering, time series analysis, anomaly detection, natural language processing (NLP), topic modeling, and generative AI. This individual will be pivotal in enhancing the team’s capacity to conceptualize, develop, and deploy complex models efficiently, within project timelines.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field required.
7 years of professional full-time data science experience, with a proven track record of developing and deploying production-grade ML models.
Candidates should have experience with marketing attribution and optimization modeling.
Advanced proficiency in Python and SQL (Snowflake preferred), with significant hands-on experience.
Deep expertise in ML Ops and experience managing production model workflows.
Proven application of a variety of advanced data science techniques beyond classification and regression, including unsupervised learning, clustering, time series analysis, anomaly detection, NLP, topic modeling, and generative AI.
Demonstrated ability to develop and deploy models while meeting project deadlines, with examples of successful implementations.
High level of business acumen, with practical experience in client-facing roles or sales preferred.
Exceptional communication skills for explaining complex data insights to diverse audiences.
Ability to foster a culture of collaboration, continuous improvement, and innovation.
Computer competency and ability to work with a computer.
Prioritize multiple tasks and projects simultaneously.
Exceptional written and verbal communication skills.
Punctuality expected, ready to report to work on a consistent basis.
Attain and maintain high performance expectations on a monthly basis.
Work in a fast-paced, high-volume setting.
Use and navigate multiple computer systems with exceptional multi-tasking skills.
Remain calm and professional during difficult discussions.
Take constructive feedback.
Available for full-time position.

Nice To Haves

A master’s or Ph.D. is preferred.
Experience in financial services or a related industry (Preferred).
Experience with data warehouse solutions like Snowflake, Azure, Redshift, or GCP (Preferred).
Extensive experience with DataRobot or similar platforms is highly preferred.
Experience in marketing optimization models is a plus.
Familiarity with data visualization tools for impactful presentations.
Exposure to collaborative tools such as JIRA and Miro.

Responsibilities

Serve as a strategic addition to the data science team, contributing advanced technical expertise to complement existing skills.
Conceptualize, develop, and deploy production-grade ML models and AI applications, consistently delivering projects within 8–12-week timeframes.
Apply sophisticated data science techniques, including unsupervised learning, clustering, time series analysis, anomaly detection, NLP, topic modeling, and generative AI, to address business challenges.
Implement and maintain ML Ops practices to streamline and manage the full model lifecycle for scalable, production-ready solutions.
Collaborate with cross-functional teams to identify business needs and translate them into impactful data science solutions.
Use strong SQL (Snowflake preferred) and Python skills for data preparation, analysis, and model development.
Demonstrate high business acumen by understanding the practical implications of data science projects and communicating effectively with stakeholders.
Present complex analyses and results to both technical and non-technical audiences, ensuring clarity and actionable insights.
Mentor junior team members, contributing to a culture of continuous learning and data innovation.