PSignite

Data Scientist

Brak informacji o wynagrodzeniu

MidZdalnieFull-time

#460050·Dodano 14 dni temu·9

Źródło: nofluffjobs.com

Aplikuj teraz

Tech Stack / Keywords

DegreeData scienceMachine Learning Python pandas NumPy scikit-learnForecastingGitOOPSQL LinuxCloudAWSAIExcelData analysis

Podsumowanie oferty

Join the Data Science team building scalable machine learning models for the CPG and Retail industries. Develop and deploy models that forecast sales volume, measure promotion effectiveness, and analyze price elasticity of demand, using Python, pandas, NumPy, and scikit-learn.

Wymagania

Degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related field.
At least 1 year of commercial experience as a Data Scientist or Machine Learning Engineer.
Proficiency in Python and core data and machine learning libraries, including pandas, NumPy, scikit-learn, and statsmodels.
Hands-on experience with gradient boosting models.
Solid understanding of time-series forecasting, regression modeling, and cross-validation techniques.
Knowledge of regression evaluation metrics in business contexts, such as MAE, R-squared, and NRMSE.
Experience with version control systems such as Git and clean, modular coding practices using OOP.
Proficiency in SQL.
Comfortable working in a Linux environment.
Ability to present and communicate results to technical and business stakeholders, translating model outcomes into clear business value.
Communicative English, sufficient for reading documentation and presenting results.

Nice to have:

Familiarity with CPG/FMCG or Retail data and domain-specific metrics.
Experience with cloud environments, preferably AWS.
Practical use of Explainable AI tools, such as SHAP.
Experience with hyperparameter optimization frameworks, such as Optuna.
Working knowledge of Excel for ad-hoc data analysis and reporting.

Obowiązki

Predictive Modeling:

Build and improve sales volume forecasting models using gradient boosting methods and regression techniques, including hyperparameter optimization.

Data Analysis & Feature Engineering:

Explore and prepare data for modeling, including time-series feature creation, promotion and pricing analysis, outlier detection, and data quality assurance.

MLOps:

Operationalize models and work within a production-grade machine learning pipeline leveraging cloud infrastructure.

Explainable AI (XAI):

Collaborate with business teams to interpret and communicate model outputs, translating analytical results into actionable business insights.

Research:

Test new hypotheses, evaluate alternative loss functions and algorithms, and explore improvements to existing modeling approaches.
Pursue independent research topics, with the opportunity to publish findings of novel scientific value.

Benefity

Opportunity to work with large analytical datasets and contribute to decisions made by major corporations.
Structured research process from exploration through to production deployment.
Access to cloud computing infrastructure and a modern technology stack.
Opportunity to conduct independent research and publish results of scientific value.
Mentorship support.
Cofinancing of a Multisport card.
Private medical care.

Karta sportowa

Opieka zdrowotna

Inne informacje

Fully remote work includes on-site collaboration for 2–3 days per quarter.

PSignite

Pracodawca

Aplikuj teraz