Data Analyst / Data Scientist 4-8 Years - USC/Gc/H4EAD

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.

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.