Accountabilities
Translate business questions and stakeholder needs into well-defined analytical problems, communicating findings, assumptions, limitations, and trade-offs clearly to technical and non-technical audiences.Explore, clean, validate, and integrate data from multiple sources to create reliable datasets and meaningful features for statistical analysis and machine learning.Design, develop, train, and evaluate statistical and machine learning models, selecting appropriate techniques that balance performance, interpretability, and business requirements.Design and analyze experiments, including A/B tests, and establish relevant metrics to measure business impact and support data-driven decision-making.Collaborate with data and engineering teams to deploy analytical solutions and machine learning models into production, ensuring they remain reliable and are monitored over time.Write clean, tested, maintainable, and reproducible code, documenting methodologies, assumptions, experiments, and results to support transparency and knowledge sharing.Partner with project leadership and client stakeholders to align analytical priorities with business objectives and deliver insights that inform strategic and operational decisions.Track project progress, communicate results effectively, identify potential risks, and raise blockers early to ensure timely and successful delivery.Continuously evaluate analytical approaches and model performance, identifying opportunities to improve accuracy, efficiency, and business relevance.Requirements
At least four years of professional experience as a Data Scientist or in a comparable applied machine learning, statistical modeling, or advanced analytics role.Strong proficiency in Python, including data analysis and machine learning libraries such as pandas and scikit-learn, or equivalent tools.Solid SQL skills and experience extracting, transforming, and analyzing data from multiple sources.Strong foundations in statistics and machine learning, including model selection, validation techniques, performance evaluation, and experimental design.Demonstrated experience taking analytical projects or machine learning models from initial exploration through implementation, delivering solutions that stakeholders can actively use.Ability to translate complex technical findings into clear, actionable recommendations for both technical teams and business stakeholders.Advanced English proficiency, both written and spoken, with the ability to collaborate daily with international clients and cross-functional teams.Strong analytical thinking, problem-solving abilities, attention to detail, and a pragmatic approach to selecting solutions that address real business needs.Excellent communication and collaboration skills, with the ability to work independently, document work effectively, and proactively communicate progress and risks.Nice to have:
Experience in e-commerce, retail, or manufacturing, particularly in areas such as demand forecasting, pricing optimization, customer analytics, or related business applications.Experience deploying and monitoring machine learning models in production using MLOps practices and cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.Familiarity with modern data stack technologies, including dbt, Snowflake, BigQuery, or Databricks.Experience with business intelligence and data visualization tools such as Tableau, Looker, or Power BI.Previous experience in consulting or other client-facing environments, translating business requirements into practical data science solutions.Candidates who do not meet every listed requirement are still encouraged to apply. A growth mindset, adaptability, and enthusiasm for learning are highly valued. Fluent English is essential for daily collaboration with international stakeholders.
Benefits
Flexible, fully remote working environment.Career development opportunities within a growing technology consultancy.Opportunity to work on high-impact data science initiatives for a major U.S.-based e-commerce business.Exposure to real-world machine learning applications, advanced analytics, and business experimentation.Collaboration with international stakeholders and multidisciplinary data and engineering teams.Opportunities to contribute to end-to-end data science projects, from initial analysis through production implementation.A dynamic environment that values continuous learning, innovation, and professional growth.