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Data Analyst

Data Analyst at Hudson Data · Remote only · 6 - 10 years · ₹9L - ₹12L / yr · Profitable · Remote only · Posted 19 Jan 2026

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Data Analyst

MadanLal Gupta's profile picture
Posted by MadanLal Gupta
6 - 10 yrs
₹9L - ₹12L / yr
Remote only
Skills
skill iconPython
SQL
skill iconGoogle Analytics
Linux/Unix
Google Cloud Platform (GCP)
skill iconGitHub

About Hudson Data


At Hudson Data, we view AI as both an art and a science. Our cross-functional teams — spanning business leaders, data scientists, and engineers — blend AI/ML and Big Data technologies to solve real-world business challenges. We harness predictive analytics to uncover new revenue opportunities, optimize operational efficiency, and enable data-driven transformation for our clients.


Beyond traditional AI/ML consulting, we actively collaborate with academic and industry partners to stay at the forefront of innovation. Alongside delivering projects for Fortune 500 clients, we also develop proprietary AI/ML products addressing diverse industry challenges.


Headquartered in New Delhi, India, with an office in New York, USA, Hudson Data operates globally, driving excellence in data science, analytics, and artificial intelligence.



About the Role


We are seeking a Data Analyst & Modeling Specialist with a passion for leveraging AI, machine learning, and cloud analytics to improve business processes, enhance decision-making, and drive innovation. You’ll play a key role in transforming raw data into insights, building predictive models, and delivering data-driven strategies that have real business impact.



Key Responsibilities


1.⁠ ⁠Data Collection & Management

• Gather and integrate data from multiple sources including databases, APIs, spreadsheets, and cloud warehouses.

• Design and maintain ETL pipelines ensuring data accuracy, scalability, and availability.

• Utilize any major cloud platform (Google Cloud, AWS, or Azure) for data storage, processing, and analytics workflows.

• Collaborate with engineering teams to define data governance, lineage, and security standards.


2.⁠ ⁠Data Cleaning & Preprocessing

• Clean, transform, and organize large datasets using Python (pandas, NumPy) and SQL.

• Handle missing data, duplicates, and outliers while ensuring consistency and quality.

• Automate data preparation using Linux scripting, Airflow, or cloud-native schedulers.


3.⁠ ⁠Data Analysis & Insights

• Perform exploratory data analysis (EDA) to identify key trends, correlations, and drivers.

• Apply statistical techniques such as regression, time-series analysis, and hypothesis testing.

• Use Excel (including pivot tables) and BI tools (Tableau, Power BI, Looker, or Google Data Studio) to develop insightful reports and dashboards.

• Present findings and recommendations to cross-functional stakeholders in a clear and actionable manner.


4.⁠ ⁠Predictive Modeling & Machine Learning

• Build and optimize predictive and classification models using scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, and H2O.ai.

• Perform feature engineering, model tuning, and cross-validation for performance optimization.

• Deploy and manage ML models using Vertex AI (GCP), AWS SageMaker, or Azure ML Studio.

• Continuously monitor, evaluate, and retrain models to ensure business relevance.


5.⁠ ⁠Reporting & Visualization

• Develop interactive dashboards and automated reports for performance tracking.

• Use pivot tables, KPIs, and data visualizations to simplify complex analytical findings.

• Communicate insights effectively through clear data storytelling.


6.⁠ ⁠Collaboration & Communication

• Partner with business, engineering, and product teams to define analytical goals and success metrics.

• Translate complex data and model results into actionable insights for decision-makers.

• Advocate for data-driven culture and support data literacy across teams.


7.⁠ ⁠Continuous Improvement & Innovation

• Stay current with emerging trends in AI, ML, data visualization, and cloud technologies.

• Identify opportunities for process optimization, automation, and innovation.

• Contribute to internal R&D and AI product development initiatives.



Required Skills & Qualifications


Technical Skills

• Programming: Proficient in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, H2O.ai).

• Databases & Querying: Advanced SQL skills; experience with BigQuery, Redshift, or Azure Synapse is a plus.

• Cloud Expertise: Hands-on experience with one or more major platforms — Google Cloud, AWS, or Azure.

• Visualization & Reporting: Skilled in Tableau, Power BI, Looker, or Excel (pivot tables, data modeling).

• Data Engineering: Familiarity with ETL tools (Airflow, dbt, or similar).

• Operating Systems: Strong proficiency with Linux/Unix for scripting and automation.


Soft Skills

• Strong analytical, problem-solving, and critical-thinking abilities.

• Excellent communication and presentation skills, including data storytelling.

• Curiosity and creativity in exploring and interpreting data.

• Collaborative mindset, capable of working in cross-functional and fast-paced environments.



Education & Certifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.

• Master’s degree in Data Analytics, Machine Learning, or Business Intelligence preferred.

• Relevant certifications are highly valued:

• Google Cloud Professional Data Engineer

• AWS Certified Data Analytics – Specialty

• Microsoft Certified: Azure Data Scientist Associate

• TensorFlow Developer Certificate



Why Join Hudson Data


At Hudson Data, you’ll be part of a dynamic, innovative, and globally connected team that uses cutting-edge tools — from AI and ML frameworks to cloud-based analytics platforms — to solve meaningful problems. You’ll have the opportunity to grow, experiment, and make a tangible impact in a culture that values creativity, precision, and collaboration.


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About Hudson Data

Founded :
2013
Type :
Services
Size :
0-20
Stage :
Profitable

About

We offer agile AI/ML building blocks and thought leadership to assist you in discovering, implementing, and augmenting intelligent decision-making.
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Manohar Kumar

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