Data Scientist

Role Overview

Gradera is seeking a Data Scientist to turn complex real-world data into insights and scalable machine learning solutions. You’ll work across the data lifecycle—partnering with data engineering and business teams to explore, clean, and understand data, then translating findings into models, experiments, and data-driven recommendations.

Responsibilities

• Collect, clean, and analyze large structured and unstructured datasets from internal and external sources
• Perform exploratory data analysis (EDA): distributions, relationships, outliers, and missing value patterns
• Profile and audit datasets for quality, completeness, consistency, and modeling fitness
• Investigate and document data lineage (origins, flows, transformations)
• Identify and help resolve data anomalies and integrity issues with data engineering
• Build analytical datasets by translating raw, messy data into modeling-ready formats
• Apply statistical techniques (e.g., correlation, hypothesis testing, variance analysis, distribution fitting)
• Build and evaluate ML models across:
  • Regression, classification, clustering
  • NLP and time-series analysis
• Design and analyze A/B experiments and controlled tests using causal inference techniques
• Produce data-driven recommendations with rigorous statistical reasoning
• Write clean, production-ready code in Python or R
• Collaborate with data engineers on pipelines and feature stores
• Deploy and monitor ML models using MLOps best practices on cloud infrastructure
• Build dashboards and self-serve analytics tools for stakeholder decision-making

Requirements / Skills

• Strong ability to quickly learn unfamiliar datasets (structure, semantics, and quirks)
• Experience working with messy, incomplete, or poorly documented real-world data
• Ability to uncover patterns (trends, seasonality, anomalies) via visual and statistical exploration
• Strong data questioning skills: validate sources, challenge assumptions, and understand collection context
• Proficiency in data profiling, descriptive statistics, and dataset reporting
• Experience creating data dictionaries, documentation, and data quality reports

Nice-to-Haves (implied)

• Comfort working across structured relational data and other data types (text unstructured, semi-structured implied)