Remote: Senior Data Scientist (Media Domain)

Job Description: Senior Data Scientist (Media Domain)

Location: US (NYC Hybrid or remote)

Full time

Responsibilities

Develop and deploy advanced statistical and machine learning models across use cases such as churn prediction, audience segmentation, content recommendation, and customer lifetime value.

Translate business challenges into analytical frameworks and deliver high-impact insights that influence strategy and operations.

Analyze large, complex datasets using Python, SQL, Pyspark and statistical tools to derive meaningful patterns and trends.

Collaborate with stakeholders across marketing, product, and data teams to understand needs and deliver actionable solutions.

Build dashboards and visualizations to communicate insights clearly to cross-functional and senior leadership teams.

Stay current with the latest media analytics trends and techniques, continuously improving analytical approaches.

Mentor junior analysts and contribute to best practices in model development, validation, and deployment.

Required Qualifications

Bachelor’s or Master’s degree in Statistics, Computer Science, Economics, or a related quantitative field.

3–8 years of experience in data science or advanced analytics, with at least 2–3 years in the media or entertainment domain.

Proven ability to build and implement models for customer behavior, retention, personalization, or marketing optimization.

Proficiency in Python, SQL, and statistical/ML libraries (e.g., scikit-learn, XGBoost, stats models).

Strong storytelling and communication skills with experience presenting to business stakeholders.

Ability to independently manage analytics projects and collaborate across technical and business teams.

Preferred Qualifications

Experience deploying models on cloud platforms such as AWS, GCP, or Azure.

Deep understanding of media domain metrics, user behavior, and content performance drivers.

Exposure to Generative AI applications and large-scale personalization in media use cases.

Proven ability to collaborate with global stakeholders in cross-functional, matrixed environments.

Familiarity with productionizing models and working closely with MLOps or engineering teams.