Senior Manager, Business Intelligence and Data Analytics

Centric Brands•New York, NY

4d•$125,000 - $140,000•Hybrid

About The Position

Centric Brands is a leading lifestyle brand collective that designs, sources, markets and sells high quality products in multiple segments, including women’s, men’s and kid’s apparel, accessories, entertainment and beauty. Centric Brands is focused on our customers and our brands that will drive the company’s future growth. We are defined by innovation as we seize new opportunities and thrive in an environment informed by creativity and thinking that is both analytical and outside the box. Centric Brands reflects a team built on respect, for others and for the hard work it takes to achieve our goals and build our bright future together. Position Overview: We are looking for a commercially minded business leader with a strong data science foundation to bridge the gap between raw data and strategic decision-making. This hybrid role demands both the analytical rigor of a data scientist and the business acumen of a planning leader — someone who can wrangle large, complex datasets, build statistical models and tools that drive insight, and translate findings into clear, actionable recommendations for stakeholders at all levels. You will sit at the intersection of data engineering, statistical analysis, and business strategy — owning the full journey from raw data ingestion through to boardroom-ready insight.

Requirements

3-6 years of experience in a business analyst, data analyst, or data scientist role.
Must have experience in the Apparel or CPG space.
Must have Knowledge of retail, supply chain, or e-commerce analytics (inventory forecasting, allocation modelling, demand planning, retail planning).
Proficiency in Python/R for statistical analysis, modelling, and data manipulation.
Strong SQL skills with experience querying large relational databases and data warehouses.
Demonstrable experience building and validating statistical models in a commercial context.
Experience working with big data tools and cloud platforms (e.g. Snowflake, BigQuery, Spark, Databricks).
Ability to produce compelling data visualizations and dashboards using tools such as Tableau, Power BI, or Sigma.
Strong communication skills — able to present analytical findings clearly to non-technical stakeholders.
Bachelor's degree or higher in Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.

Nice To Haves

Experience with SAP APO or IBP is desired

Responsibilities

Partner with planning, commercial, and finance teams to define tool and analytical requirements and translate business questions into structured data problems.
Produce insight reports, dashboards, and executive-ready presentations that distil complex findings into clear narratives. Scope includes SIOP process, and both standardized and self service analytics initiatives.
Identify trends, anomalies, and opportunities within business data and proactively surface findings to relevant stakeholders.
Own the end-to-end delivery of analytical projects — from scoping and data acquisition through to model deployment and stakeholder sign-off.
Own and maintain the replenishment system’s demand forecasting models.
Design, build, and validate statistical models including regression (linear, logistic, ridge/lasso), time-series forecasting, clustering, and classification.
Apply hypothesis testing, A/B testing frameworks, and experiment design to support data-driven decision-making.
Assess model performance using appropriate metrics and iterate to improve accuracy and reliability.
Run product segmentation analysis to drive improved forecasting
Document modelling assumptions, limitations, and confidence intervals clearly for both technical and non-technical audiences.
Extract, transform, and load large datasets from disparate sources including data warehouses, APIs, and third-party platforms.
Reduce data complexity through dimensionality reduction techniques and aggregation strategies to produce manageable, analysis-ready datasets.
Optimize SQL queries and data pipelines to improve performance across high-volume datasets.
Work with cloud data platforms (e.g. Snowflake, BigQuery, Databricks) to manage and process big data at scale.
Implement data quality checks, validation logic, and anomaly detection to ensure the integrity of analytical outputs.
Act as a trusted analytical partner to senior stakeholders — translating their business objectives into data-driven frameworks.
Present findings to non-technical audiences with confidence, using data storytelling techniques and visualisation tools.
Collaborate with engineering and data teams to ensure analytical models are production-ready and maintainable.
Contribute to building a data-literate culture across the organisation through documentation, knowledge-sharing, and internal training.
Support the development of self-service BI analytic tools, dashboards and standardized reports that enable business teams to independently and efficiently monitor performance and identify opportunities.