This role is designed for an experienced data scientist who thrives on building impactful, data-driven solutions that enhance user experience and engagement. You will lead initiatives in personalization, recommendation systems, and targeting algorithms, translating complex data into actionable insights. Collaborating closely with cross-functional teams—including Product, Engineering, Marketing, and Data Engineering—you will design, implement, and monitor machine learning models at scale. Your work will directly influence user retention, engagement, and the overall growth of digital products. The ideal candidate combines strong analytical skills with hands-on machine learning experience and a passion for leveraging data to solve real-world problems. This position offers an innovative, fast-paced, and collaborative environment where your contributions drive measurable business impact.

Accountabilities:

Design, develop, and deploy machine learning models including recommendation systems, targeting algorithms, segmentation, and ranking models
Lead personalization initiatives spanning modeling, experimentation, and implementation to enhance user experience and retention
Design, execute, and analyze A/B tests and other experiments to evaluate the effectiveness of personalization strategies
Collaborate cross-functionally with Product, Engineering, Marketing, and Data Engineering to integrate models into production
Develop clean, maintainable code and contribute to reusable pipelines, feature stores, and evaluation frameworks
Translate data insights into actionable strategies and compelling narratives for both technical and non-technical stakeholders

Requirements

Bachelor's degree in Mathematics, Physics, Statistics, Economics, Computer Science, or a related field; MS preferred
2+ years of experience in data science, machine learning, or a related technical role
Hands-on experience with recommendation engines, targeting systems, ranking models, or personalization algorithms
Strong proficiency in Python for modeling and data manipulation. Advanced SQL skills for querying large, complex datasets
Solid foundation in statistics, hypothesis testing, and experimental design
Familiarity with cloud-based platforms and tools such as AWS, GCP, Snowflake, dbt, or Airflow
Proven ability to partner cross-functionally and influence product decisions using data-driven insights
Bonus skills: experience with uplift modeling, multi-armed bandits, causal inference, real-time personalization pipelines, or tools like MLflow, SageMaker, and Feature Stores

Benefits

Competitive base salary range: $180,000 - $210,000 USD, plus target equity
Unlimited PTO for full-time employees
16 weeks of fully paid parental leave
$500 home office allowance
Remote-first culture with a highly engaged distributed workforce
5% 401(k) match and access to FSA
Company-paid health, dental, and vision plans for employees and dependents
Opportunities to work on high-impact projects in an innovative, collaborative environment

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.

🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.

📊 It compares your profile to the job's core requirements and past success factors to determine your match score.

🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.

🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!