H4EAD

Hudson Manpower•Houston, TX

8d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

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.