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 Collaborate with data engineers to build reliable data pipelines and feature stores Deploy and monitor ML models using MLOps best practices on cloud infrastructure Build dashboards and self-serve analytics tools to support stakeholder decision-making Data Understanding & Analysis Skills Strong ability to interrogate unfamiliar datasets and quickly develop a working understanding of their structure, semantics, and quirks Experience working with messy, incomplete, or poorly documented real-world data Skilled in identifying hidden patterns, trends, seasonality, and anomalies through visual and statistical exploration Ability to ask the right questions about data — challenging assumptions, validating sources, and understanding the context in which data was collected Proficiency in data profiling, descriptive statistics, and summary reporting to communicate the shape and health of a dataset Experience creating data dictionaries, documentation, and data quality reports to support team-wide data understanding Comfort working across structured (relational tables), semi-structured (JSON, XML), and unstructured (text, logs, sensor streams) data formats Technical Skills Required Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch or TensorFlow) and/or R Strong SQL skills with hands-on experience in DB2 and SQL Server Experience with Databricks for large-scale data processing, feature engineering, and model training Familiarity with cloud platforms: Azure or AWS Experience with data warehouses and big data platforms (Databricks, Snowflake, or Redshift) Knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow Experience with streaming data technologies such as Kafka or Spark Solid foundation in probability, statistics, linear algebra, and experimental design Nice to Have Experience with deep learning, NLP, computer vision, or Bayesian methods Familiarity with real-time or streaming data pipelines Open-source contributions or published research