Data Scientist

Cortland; Atlanta (GA); On-site; USD 110,000 - 165,000; Full time

Job summary

Cortland is seeking a Data Scientist to design and deploy advanced analytics and ML solutions that address investment and operational challenges. You’ll transform complex data into production-ready insights that drive smarter decisions and Cortland’s growth.

Responsibilities include partnering with stakeholders to prioritize use cases, building models, deploying production pipelines, and communicating insights with clarity to diverse audiences across the organization.

Qualifications

• Bachelor’s degree in a STEM field; advanced degree preferred.
• 2+ years applying data science or advanced analytics in business.
• Strong foundation in statistical modeling, ML, clustering and optimization.
• Proficiency in Python and SQL; Spark/Scala a plus.
• Experience with cloud platforms (AWS or Azure) and data tools like Databricks, EMR, RDS, S3.
• Ability to handle large datasets and explain analytical approaches clearly.
• Excellent communication and data storytelling skills.

Responsibilities

• Partner with stakeholders to identify and prioritize high-value predictive and optimization use cases.
• Design, build, and maintain ML models to support operations and investment strategies.
• Collaborate with data and engineering teams to establish production analytics pipelines.
• Develop end-to-end solutions including model development, deployment, monitoring, and improvement.
• Leverage BI, GIS, and visualization tools to communicate insights to technical and non-technical audiences.
• Serve as technical lead for AI initiatives and educate teams on adoption.
• Stay current on data science tools and methodologies and bring innovations to Cortland.

Skills

Python

SQL

Statistics

Machine learning

Communication

Data visualization

Education

Bachelor's degree in STEM

Advanced degree preferred

Tools

Spark

Scala

Databricks

AWS

Azure

EMR

RDS

S3

Job description

As a Data Scientist, you’ll design and deploy advanced analytics and machine learning solutions that address critical investment and operational challenges. Your work will transform complex data into scalable, production‑ready insights that drive smarter decisions and accelerate Cortland’s growth.

• Partner with business stakeholders to identify, prioritize, and deliver high‑value predictive and optimization use cases.
• Design, build, and maintain machine learning and statistical models that support ongoing operational and investment strategies.
• Collaborate with data and engineering teams to establish the infrastructure, pipelines, and processes required for production analytics.
• Develop end‑to‑end solutions, including model development, deployment, monitoring, and continuous improvement.
• Leverage BI, GIS, and visualization tools to clearly communicate insights to technical and non‑technical audiences.
• Serve as the technical lead for AI/agentic initiatives while educating the broader teams to effectively adopt.
• Stay current on emerging data science tools, platforms, and methodologies, bringing relevant innovations into the organization.

Qualifications

• Bachelor’s degree in a STEM‑related field (e.g., statistics, applied math, computer science, engineering, business analytics) required; Advanced degree preferred.
• 2+ years of professional experience applying data science or advanced analytics in a business environment
• Strong foundation in statistical modeling, machine learning, clustering, classification, recommendation systems, and optimization techniques
• Proficiency in Python and SQL; experience with Spark, Scala, or similar tools is a plus
• Working knowledge of cloud platforms and data ecosystems (AWS or Azure), including tools such as Databricks, EMR, RDS, and S3
• Experience working with large, complex datasets and resolving performance or memory constraints
• Solid business intuition with the ability to explain analytical approaches, tradeoffs, and outcomes clearly
• Excellent communication skills, with the ability to present insights visually and tell a compelling data‑driven story