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.
