Data Scientist

About the role

about sciemo

Sciemo builds AI for consumer goods: technology that helps businesses make faster, smarter, and more human decisions across the entire IBP process. From optimizing promotions to balancing demand and supply, Sciemo's platform transforms messy, siloed data into measurable business impact. Its AI agents assist decision-makers in real-time, turning complexity into clarity. We are headquartered in New York City.

ROLE

As a Data Scientist, you will build the models that turn customer data into decisions — forecasting, optimization, measurement, and the analysis that tells us whether any of it is working.

You'll work across the full lifecycle: understanding a business problem, exploring the data, developing and validating models, and partnering with engineers to get them into production. You'll also work directly with customer data and customer-facing teams, which means your analysis needs to hold up under scrutiny from people whose decisions depend on it.

This role is a strong fit for someone earlier in their career who wants real ownership quickly. You'll get direct mentorship from our Co-Founder & CAIO and senior engineers, and the scope to grow fast.

RESPONSIBILITIES

Modeling & Analysis

• Develop, validate, and iterate on models for forecasting, optimization, anomaly detection, and measurement.

• Perform exploratory analysis on complex customer datasets to surface patterns worth acting on.

• Design and analyze experiments; build the measurement approaches that quantify business impact.

• Establish rigorous validation practices — backtesting, holdouts, and honest error analysis.

Production & Collaboration

• Partner with ML and data engineers to move models from notebook to production.

• Contribute to feature engineering, evaluation pipelines, and model monitoring.

• Write clean, reproducible Python that other people can read and build on.

Business Impact

• Translate business questions into analytical problems, and analytical results back into recommendations.

• Communicate findings clearly to internal teams and customers, including non-technical audiences.

• Follow deployed models into the real world and help make sure they're delivering what we promised.

ALL ABOUT YOU

• Experience developing and validating models on real, messy data — through internships, prior roles, or substantive project work.

• Solid foundation in statistics and machine learning: regression, time series, tree-based methods, experimental design.

• Strong Python (pandas, scikit-learn, and the surrounding ecosystem) and strong SQL.

• Curiosity about the business problem behind the data, not just the modeling technique.

• Clear communication — you can explain what you did, why, and what it means, to someone who doesn't do this for a living.