- Company Name
- Gemini
- Job Title
- Principal Data Scientist, Machine Learning (Growth)
- Job Description
-
Job Title: Principal Data Scientist, Machine Learning (Growth)
Role Summary
Lead end‑to‑end design, development, and production of machine learning models that drive customer onboarding, product adoption, and acquisition in a global crypto and fintech environment. Own full ML lifecycle, collaborate with product, engineering, and operations, and mentor junior talent while setting technical standards.
Expectations
* Deliver high‑impact, production‑grade ML solutions that improve key growth metrics (LTV, CAC, adoption).
* Demonstrate leadership in technical strategy, code quality, and data science best practices.
* Communicate findings clearly to both technical and non‑technical stakeholders and influence product direction.
* Keep abreast of advancements in ML tooling and crypto analytics to continuously enhance the model portfolio.
Key Responsibilities
1. Analyse large, complex datasets to surface growth opportunities and engineer predictive features from internal and external sources.
2. Design, train, and deploy ML models for lifetime value forecasting, marketing channel optimisation, product cross‑sell, anomaly detection, and behavioural profiling.
3. Build and maintain end‑to‑end data and model pipelines (ETL, versioning, monitoring) using Databricks/SageMaker/Snowflake/MLflow, Airflow, Spark, etc.
4. Evaluate model performance via experiments, back‑testing, and continuous monitoring; adjust models to reduce CAC and increase adoption.
5. Partner with product managers, engineers, and customer service to translate model outputs into on‑site features and growth initiatives.
6. Mentor and review work of junior/mid‑level data scientists and ML engineers.
7. Participate in talent acquisition and workforce planning for the Machine Learning group.
8. Propose and prototype new tools or techniques to improve modeling or deployment pipelines.
Required Skills
* 10+ years data science / ML experience, 7+ years with a PhD.
* Proven track record (5+ years) of deploying production‑grade, real‑time or large‑scale ML models in financial, payments, or B2C contexts.
* Expert in Python (scikit‑learn, XGBoost, TensorFlow, PyTorch) and SQL.
* Experience with Databricks, SageMaker, Snowflake, MLflow, Airflow, Spark, and orchestration of data pipelines.
* Strong cross‑functional collaboration, stakeholder communication, and ability to translate technical insights into business action.
* Familiarity with modeling for LTV, marketing mix, recommendation, or similar growth‑focused domains.
* Knowledge of crypto/blockchain and on‑chain analytics is a plus.
Required Education & Certifications
* PhD or equivalent advanced degree in a quantitative field (computer science, statistics, mathematics, engineering, or related).
* Master’s degree preferred as alternative.
* No specific certifications required, but demonstrable experience in the above technologies and domains is mandatory.