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





· Solid 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

Skills:- Python, SQL and databricks

📌 Data Scientist (Hyderabad)
🏢 Gradera AI Technologies
📍 Hyderabad