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

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, Job Title Data Scientist Education Bachelor’s Degree Location Houston, TX 77032 US (Primary) Career Level Professional Category Operations Job Type Permanent …

• 14 days ago +

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, 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.