Data Scientist

About Gradera

Gradera is an AI‑Native Services firm pioneering Software‑Orchestrated Services™—a new enterprise transformation model where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed, scalable outcomes. We help enterprises move beyond fragmented AI pilots, disconnected automation, and labor‑led models by redesigning how work gets done across operations, product, engineering, customer experience, data, and core workflows.

Overview

We are seeking a highly analytical and curious Data Scientist to transform complex, real-world data into meaningful insights and scalable machine learning solutions. In this role, you will work across the full data lifecycle—partnering with data engineering and business teams to explore, clean, and understand diverse datasets, and translating those insights into models, experiments, and data-driven recommendations.

You will play a critical role in bridging raw data and business impact, developing a deep understanding of how data is generated, structured, and used. This includes conducting rigorous exploratory analysis, assessing data quality and lineage, and building robust analytical datasets that power advanced modeling and reporting.

This role offers the opportunity to work with large-scale data platforms, cloud infrastructure, and modern machine learning frameworks, while contributing to impactful decision-making through experimentation, analytics, and self-service data tools.

Role & Responsibilities

- Collect, clean, and analyze large structured and unstructured datasets from multiple internal and external sources

- Conduct thorough exploratory data analysis (EDA) to understand data distributions, relationships, outliers, and missing value patterns

- Profile and audit datasets to assess data quality, completeness, consistency, and fitness for modeling

- Investigate and document data lineage — understanding where data originates, how it flows, and how it transforms across systems

- Identify and resolve data anomalies, inconsistencies, and integrity issues in collaboration with data engineering teams

- Develop a deep understanding of the business domain and the underlying data that represents it — including what each field means, how it is captured, and what its limitations are

- Translate raw, messy, real-world data into clean, well-understood analytical datasets ready for modeling and reporting

- Apply statistical techniques such as correlation analysis, hypothesis testing, variance analysis, and distribution fitting to extract meaningful signals from noise

- Build and deploy machine learning models including regression, classification, clustering, NLP, and time-series analysis

- Design, evaluate, and analyze A/B experiments and controlled tests using causal inference techniques

- Develop data-driven recommendations backed by rigorous statistical reasoning

- Write clean, production-ready code in Python or R