Customer is specifically looking for involvement in model development, descriptive, predictive, and prescriptive modeling, supervised and unsupervised approaches, model implementation, and basic MLOps tasks.

Candidate should have strong, individual hands-on experience in core data science (statistics, modeling, feature engineering, and production ML) rather than primarily BI, SAP, or AI architecture work.

What you’ll do

The ideal candidate will be responsible for analyzing large datasets to extract valuable insights, build predictive models, and support data-driven decision-making across the organization.
You will work closely with cross-functional teams including engineering, product, and business stakeholders to identify opportunities and implement data solutions.
Analyze structured and unstructured data from multiple sources to identify trends and patterns.
Experience with statistical analysis and machine learning techniques.
Identify trends, patterns, and anomalies in data to uncover opportunities, address business challenges, and generate actionable insights.
Design, develop, and implement statistical models and machine learning algorithms.
Develop and implement data processing and modeling pipelines and workflows to automate data ingestion, transformation, and loading tasks.
Generate insightful reports and dashboard visualizations with Power BI and Python to communicate findings and recommendations to stakeholders.
Work with engineering teams to deploy models into production and maintain performance.
Perform data validation and ensure data accuracy and integrity throughout the modeling and analysis process.
Assist with the completion of ad-hoc data pulls or analyses that have tight turnarounds utilizing advanced programming languages.
Approach data analyses and data modeling with curiosity and a keen understanding of the business context.
Effectively communicate insights to both technical and non-technical stakeholders; be a persuasive storyteller.

Qualifications

Experienced in forecasting key retail metrics such as sales, inventory, and headcount, with strong proficiency in statistical techniques including hypothesis testing, clustering, and segmentation to support data-driven decision-making.
Experienced and proficient in Python machine learning libraries such as Scikit-Learn, PyTorch, Keras, TensorFlow, NumPy, SciPy, pandas, Matplotlib, Plotly, etc.
Proficiency in SQL and other programming languages for data analysis and modeling.
Proficient in GitHub and Repository Management.
Advanced in Excel including V-lookups, pivot tables, power pivots, and VBA.
Experience with cloud development environments: GCP.
Experience with Teradata.
Strong analytical and critical thinking skills to interpret data, identify patterns, and generate insights.

Data Scientist