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.

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.