Data Scientist

Talentrabbit; Hyderabad; Hybrid; INR 1,000,000 - 2,000,000; Full time

Job summary

Talentrabbit is seeking a data understanding and analysis specialist in Hyderabad to collect, clean, and analyze large datasets from multiple sources. You will perform EDA, profile data quality, and document data lineage to enable robust modeling and reporting.

The role requires building ML models (regression, classification, clustering, NLP, time-series) and deploying them with MLOps on cloud platforms, plus creating dashboards for stakeholders.

Qualifications

• Ability to interrogate unfamiliar datasets and understand structure and semantics.
• Experience with messy, incomplete, or poorly documented data.
• Skill in identifying patterns, trends, and anomalies through exploration.
• Ability to question data sources and validate context.
• Skills in descriptive statistics and data profiling to report data health.

Responsibilities

• Collect, clean, and analyze large structured and unstructured datasets.
• Conduct exploratory data analysis to understand distributions and patterns.
• Profile datasets for quality, completeness, and consistency.
• Document data lineage and track data flow across systems.
• Identify and resolve data integrity issues with data engineering.
• Develop understanding of business domain and data limitations.
• Translate raw data into clean analytical datasets for modeling and reporting.
• Apply statistical techniques to extract signals from noise.
• Build and deploy ML models including regression, classification, clustering, NLP, and time-series.
• Design and analyze A/B tests using causal inference techniques.
• Develop data-driven recommendations with rigorous statistical justification.
• Write clean production-ready code in Python or R.
• Collaborate with data engineers on data pipelines and feature stores.
• Deploy ML models with MLOps best practices on cloud infrastructure.
• Build dashboards and self-serve analytics for stakeholders.

Tools

Python (Pandas, NumPy, scikit-learn)

R

SQL (DB2, SQL Server)

Databricks

Azure

AWS

Data Warehouses (Snowflake, Redshift)

MLflow

Kubeflow

Airflow

Kafka

Spark

TensorFlow

PyTorch

Job description

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

Preferred candidate profile

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