What are the responsibilities and job description for the Data Scientist position at Evlo AI?
About The Role
The role focuses on translating complex business challenges into robust predictive models and analytical frameworks. The data scientist will work closely with engineering and product teams to design, validate, and scale machine learning systems that drive automated decision-making and optimization.
This position requires a practitioner who balances theoretical rigor with engineering pragmatism. The team operates in a fast-paced environment where models must not only achieve high offline accuracy but also perform reliably and efficiently when integrated into production application pipelines.
Key Responsibilities
The role focuses on translating complex business challenges into robust predictive models and analytical frameworks. The data scientist will work closely with engineering and product teams to design, validate, and scale machine learning systems that drive automated decision-making and optimization.
This position requires a practitioner who balances theoretical rigor with engineering pragmatism. The team operates in a fast-paced environment where models must not only achieve high offline accuracy but also perform reliably and efficiently when integrated into production application pipelines.
Key Responsibilities
- Develop, evaluate, and deploy predictive models using supervised and unsupervised machine learning techniques to solve high-impact business problems
- Design and execute rigorous A/B testing frameworks to validate model performance and measure incremental business impact in production
- Construct and optimize end-to-end data pipelines using SQL, Python, and PySpark to engineer features from high-dimensional, unstructured datasets
- Collaborate with MLOps and software engineers to transition experimental Python models into scalable, containerized production APIs
- Monitor deployed models for statistical drift and performance degradation, implementing automated retraining loops where necessary
- Communicate complex technical findings and model limitations to cross-functional stakeholders through clear visualizations and structured documentation
- 3–6 years of experience working as a hands-on Data Scientist, with a proven track record of deploying models into production environments
- Strong proficiency in Python and standard scientific computing libraries including Pandas, NumPy, scikit-learn, and XGBoost/LightGBM
- Expert-level SQL skills for querying, aggregating, and manipulating large-scale relational and non-relational datasets
- Experience with cloud-based machine learning platforms and data warehouses, specifically AWS (SageMaker, S3) and Snowflake or BigQuery
- Solid grounding in applied statistics, including hypothesis testing, experimental design, regression analysis, and causal inference
- BS or MS in Computer Science, Statistics, Applied Mathematics, Economics, or a related quantitative field
- Bonus: Experience with deep learning frameworks (PyTorch, TensorFlow) or implementing orchestration tools like Airflow and Prefect