Remote Data Scientist - Personalization & Recommendations

sportygroup; United States; Remote; USD 120,000 - 150,000; Full time

Benefits offered by this job

Remote first company

Competitive salary + bonuses

28 days paid annual leave

Flexible core hours 10am-3pm in local–

Top-of-the-line equipment

Annual company retreats

Job summary

Sporty is seeking a Data Scientist to build and productionize ML systems for personalization, search, and recommendations, with a clear focus on measurable business impact. Remote-first policy and flexible hours support work-life balance.

You will use Python-based modelling, cloud engineering, and scalable data pipelines to solve real-world problems at scale in a fast-paced product environment and across multiple domains.

Qualifications

• Hands-on experience applying statistical and ML models to real business problems with measurable impact
• Advanced understanding of Python and the data analysis ecosystem in Python
• Strong foundation in probability, statistics, ML, and linear algebra
• 2+ years in a data scientist, quantitative researcher, or similar role
• Knowledge of SQL and relational databases
• Proficient with AI tools as part of daily development workflow
• Degree in Applied Mathematics, CS, Financial Engineering or Engineering
• Project and stakeholder management experience is preferred

Responsibilities

• Build personalized recommendation systems and optimize product and service recommendations to improve user experience and conversion
• Develop and productionize ML/AI systems across domains including search ranking, recommendation, and content intelligence

Skills

Python

SQL

Machine learning

Data analysis

Statistical modelling

AI tools

Cloud engineering

Education

Degree in Applied Mathematics or Computer Science or Engineering

Tools

Apache Spark

AWS

GCP

Azure

Job description

Sporty is seeking a Data Scientist to build and productionize ML systems for personalization, search, and recommendations, with a clear focus on measurable business impact. Remote-first policy and flexible hours support work-life balance.

You will use Python-based modelling, cloud engineering, and scalable data pipelines to solve real-world problems at scale in a fast-paced product environment and across multiple domains.