Convert business goals into precise data science challenges with clear metrics for success

Investigate, cleanse, and merge complex datasets while crafting significant features

Develop, assess, and enhance predictive, classification, ranking, NLP, and time-series models

Plan and evaluate experiments including A/B and multi-arm tests to ensure dependable causal conclusions

Present insights through compelling narratives, visual aids, and practical recommendations

Work alongside product and engineering teams to deploy and oversee machine learning models in production environments

Create and sustain scalable dashboards and reports for real-time business tracking

Design and improve data pipelines for large-scale data ingestion, transformation, and integration

Establish, execute, and monitor success indicators for AI-based features and digital transformation initiatives

Collaborate with stakeholders to translate data findings into strategic business actions

Proven data science expertise with 5+ years in production settings

Solid grounding in statistics, probability theory, and experimental design

High proficiency in SQL and at least one programming language with machine learning libraries

Experience managing large datasets and working with distributed data processing systems

Strong skills in model evaluation, validation, and monitoring using offline and online metrics

Ability to create data visualizations and communicate effectively with executives

Understanding of MLOps practices and teamwork with data and platform engineering groups

Bachelor’s or Master’s degree in a quantitative discipline or equivalent experience

Upper-Intermediate English language ability (B2)

Background in natural language processing, forecasting, recommendation systems, or anomaly detection

Knowledge of causal inference and A/B testing techniques

Practical experience with feature stores, real-time data processing, or online ML systems

Familiarity with cloud data platforms and contemporary data warehouses

Experience mentoring others and contributing to cross-functional projects