Applied Data Scientist

Short, Gram

United States

9 days ago

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Role details

Contract type

Permanent contract

Employment type

Part-time (≤ 32 hours)

Experience level

Expert

Experience required

5 years minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

A/B Testing Data Analysis Artificial Neural Networks Data Intelligence Machine Learning Marketing Information Systems Raw Data SQL Databases Data Streaming Feature Engineering Deep Learning Information Technology

+1 more

Machine Learning Operations

Job description

Recommendation & Personalization: Own recommendation and personalization across the entire user journey: which series or episode to watch next, how series are ordered on screen, when and how notifications are sent, when a popup appears mid-viewing, which pricing offer a specific user sees - and more, as new surfaces come online.
Predictive Modeling: Turn raw behavioral data into predictive models for retention, churn, and conversion propensity. Predicted LTV (pLTV) is especially critical here - both to power personalization and as a signal we feed back to ad networks to help them find higher-quality users.
Applied Model Adaptation: Take existing models and architectures and adapt or fine-tune them to our data - applied, not from-scratch research.
Production Ownership: Ship models to production and own them end-to-end: serving, monitoring, retraining, and iteration.
Experimentation: Design and read experiments (A/B, causal) to prove real business lift, not offline metrics alone.
Cross-Functional Partnership: Partner with Product, Content, and Growth to turn model outputs into decisions.
Beyond Rule-Based Analytics: Replace hand-tuned heuristics with models that learn., * Recsys at scale: retrieval / ranking, sequence or session models, embeddings.
LTV, churn, or propensity modeling in a consumer / subscription product.
Streaming, entertainment, consumer / mobile, or growth-marketing data.
MLOps: feature stores, model serving, monitoring, retraining pipelines.
Causal inference / uplift modeling.

Requirements

BSc in a quantitative field such as Data Science, Computer Science, Statistics, or Mathematics (must). MSc or PhD is a strong plus.
5+ years of working experience in Data Science or Machine Learning, shipping models to production.
Direct, hands-on experience building recommendation and/or personalization systems in production (a firm requirement, not a nice-to-have).
Strong applied ML / data science foundations: modeling, evaluation, feature engineering.
Neural networks and deep learning (must): hands-on experience training and adapting deep learning models for real-world production use.
Time series data (must): hands-on experience modeling temporal data, including forecasting, trends, seasonality, and noisy real-world signals.
Ability to take a model or architecture and adapt it to real, messy data - practical, not purely theoretical.
Production and engineering chops: get models serving reliably and keep iterating.
Experimentation discipline: A/B testing, causal reasoning, honest metrics.
Data fluency: SQL and large data warehouses, comfortable in the raw data.
Clear communication and a bias to ship.

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