Job Description

We are seeking experienced Data Analytics / Data Science professionals with 4–8 years of hands-on experience in data analysis, statistical modeling, business intelligence, and/or machine learning. The ideal candidate will have strong expertise in SQL, Python, data visualization, statistical analysis, data modeling, and modern cloud-based data platforms, with the ability to translate complex datasets into actionable business insights.

Experience with modern AI/ML, Generative AI, LLMs, and AI-assisted analytics is highly desirable.

Experience: 4–8 Years
Employment Type: Full-Time W2 Only
Work Authorization: U.S. Citizen / Green Card / H4 EAD
Location: Open to opportunities across the United States
Relocation: Must be willing to relocate anywhere in the U.S. for a suitable opportunity

Key Responsibilities

• Collect, clean, transform, and analyze structured and unstructured data.

• Perform Exploratory Data Analysis (EDA) and identify trends, patterns, anomalies, and business opportunities.

• Develop dashboards, reports, and data visualizations using Power BI, Tableau, or equivalent tools

• Write complex and optimized SQL queries for data extraction and analysis.

• Develop statistical models and machine learning solutions for business problems.

• Build and evaluate predictive models using appropriate ML algorithms.

• Perform feature engineering, model validation, and performance evaluation.

• Work with large-scale datasets using modern data processing technologies.

• Collaborate with data engineers, software engineers, product teams, and business stakeholders.

• Communicate analytical findings and recommendations to technical and non-technical stakeholders.

• Support data quality, governance, validation, and documentation initiatives.

• Deploy and monitor analytical or machine learning models in production environments where applicable.

• Leverage AI/GenAI tools to improve data analysis, reporting, automation, and productivity.
  

Cloud & Modern Data Technologies

Experience with one or more of the following:

• AWS, Microsoft Azure, or Google Cloud Platform (GCP)

• Snowflake, Databricks, BigQuery, Redshift, or Azure Synapse

• Cloud-based data warehouses and data lakes

• Apache Spark / PySpark

• ETL/ELT tools and modern data pipeline technologies

• Airflow, dbt, or equivalent data orchestration/transformation tools

• Data lakehouse architecture and distributed data processing

AI / Machine Learning / GenAI

Experience with the following is highly desirable:

• Machine Learning using Scikit-learn, XGBoost, TensorFlow, or PyTorch

• Generative AI and LLM-based applications

• Experience working with OpenAI, Azure OpenAI, Amazon Bedrock, Google Vertex AI, or equivalent AI platforms

• RAG (Retrieval-Augmented Generation) concepts

• Embeddings and vector databases

• AI-powered analytics and intelligent automation

• LLM prompt engineering and evaluation

• Familiarity with LangChain, LlamaIndex, or similar frameworks

• Experience using AI coding/analytics assistants such as GitHub Copilot or equivalent tools

Data Engineering & Analytics Exposure

• Experience working with large and complex datasets.

• Understanding of data pipelines, ETL/ELT, data ingestion, transformation, and orchestration

• Exposure to Kafka or other event-streaming technologies is a plus.

• Understanding of data governance, lineage, security, and data quality practices.

• Experience with APIs and integrating data from multiple sources is desirable.
  

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Technology, or a related field.

• Experience building end-to-end analytics or data science solutions.

• Experience deploying ML models or analytical applications to cloud environments.

• Knowledge of MLOps and model lifecycle management.

• Experience with MLflow, Kubeflow, or equivalent platforms.

• Understanding of responsible AI, model monitoring, and AI governance.

• Experience presenting analytical insights to senior stakeholders.

Requirements

Required Skills

• 4–8 years of professional experience in Data Analytics, Data Science, Business Intelligence, or a related field.

• Strong proficiency in SQL, including complex queries, joins, CTEs, window functions, aggregations, and query optimization.

• Strong hands-on experience with Python for data analysis and/or data science.

• Experience with Pandas, NumPy, Matplotlib, Seaborn, or equivalent Python libraries.

• Strong understanding of statistics, probability, hypothesis testing, regression, and statistical analysis

• Experience with data visualization and BI tools, such as Power BI, Tableau, Looker, or similar.

• Understanding of data modeling, ETL/ELT concepts, data quality, and data pipelines

• Experience with machine learning concepts and frameworks, including Scikit-learn or equivalent.

• Experience working with relational databases such as PostgreSQL, MySQL, SQL Server, Oracle, or similar.

• Strong analytical, problem-solving, and communication skills.

• Experience working in Agile/Scrum environments.

Core Technology Stack

Python | SQL | Pandas | NumPy | Scikit-learn | PySpark | Power BI | Tableau | AWS | Azure | GCP | Snowflake | Databricks | BigQuery | Spark | Airflow | dbt | Machine Learning | Generative AI | LLMs | RAG | Vector Databases | Git

Candidate Requirements

• 4–8 years of hands-on professional experience in Data Analytics/Data Science or related roles.

• Must be authorized to work in the U.S. as a U.S. Citizen, Green Card holder, or H4 EAD holder

• W2 only.

• Must be willing to relocate anywhere in the United States for a suitable opportunity.

• Strong communication and stakeholder-management skills.

• Ability to work independently as well as collaboratively in cross-functional teams.