Barback, IT​/Tech

We are looking for a Senior Data Scientist to lead the development of end-to-end data science and machine learning solutions that directly impact product, operational, and business performance. In this role, you will leverage statistical modeling, predictive analytics, and scalable machine learning algorithms to transform complex datasets into actionable strategies and production-ready applications. You will work closely with cross-functional partners in Product, Engineering, and Operations to solve unstructured business problems, mentor junior team members, and drive a culture of data-driven decision-making.

Design, prototype, build, and deploy production-grade machine learning models, statistical algorithms, and predictive framework solutions to drive core business metrics.

• Conduct exploratory data analysis, data extraction, normalization, feature engineering, and preprocessing on high-volume structured and unstructured datasets.
• Monitor, validate, and optimize model performance in production to ensure continuous reliability, low latency, and accuracy.

Product Analytics & Experimentation

• Define robust experimentation frameworks, continuous hypothesis testing (A/B testing), and causal inference methodologies to measure the impact of product changes.
• Formulate key performance indicators (KPIs) and build clear, interactive dashboards to communicate actionable trends and strategic insights to senior stakeholders.

Cross-Functional Leadership & Strategy

• Collaborate with product managers, data engineers, and software architects to translate business objectives into technical data science roadmaps and pipeline requirements.
• Mentor junior data scientists and analysts, setting technical standards, model documentation guidelines, and coding best practices across the team.

Programming

Languages:

Advanced proficiency in Python or R for statistical analysis and machine learning.

• Data Retrieval & Manipulation: Deep mastery of SQL (complex queries, window functions, query optimization) and data manipulation libraries (e.g., Pandas, Num Py).
• Machine Learning Frameworks: Strong experience with ML toolkits and frameworks such as Scikit-Learn, XGBoost, PyTorch, or Tensor Flow.
• Data Engineering & Infrastructure: Hands-on experience with cloud platforms (AWS, GCP, or Azure), data warehouses (Snowflake, Big Query, or Redshift), and pipeline tools (Airflow, Spark).
• Methodologies: Expertise in supervised/unsupervised learning, time-series forecasting, regression analysis, clustering, and experimental design.

Soft Skills & Domain Requirements

• Excellent written and verbal communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.
• Strong business acumen and problem-solving skills to balance trade-offs between prediction accuracy, execution speed, and business value.

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field.
• Experience: 4+ years of professional experience in data science or machine learning engineering, with proven success delivering production models at scale.
• Experience with MLOps practices, containerization (Docker, Kubernetes), model tracking tools (MLflow), and CI/CD workflows.

• Salary Range: $140,000 - $170,000 annually (commensurate with experience).
• Full health, dental, and vision insurance coverage.
• Flexible paid time off (PTO) and company holidays.
• Professional development stipend and learning opportunities.