Sr. Architect - Data Science / Machine Learning
US client via staffing partner · Chicago, IL
- Location
- Chicago, IL · Hybrid
- Salary band
- $65 – $65/hr
- Type
- Contract
- Level
- Senior
- Work authorization
- Not stated
Stack
Python · Data Science · Machine Learning · Exploratory Data Analysis (EDA) · Feature Engineering · Model Lifecycle Management · Data Ingestion · Data Governance · Distributed Storage · Stream Processing · Orchestration Tools · Version Control · CI/CD · Data Visualization · Supervised Learning · Unsupervised Learning · Model Evaluation · Technical Documentation
About the role
Job Title: SR. ARCHITECT Vendor Pay Rate: $65 W2 Work Model: Hybrid Location: Chicago, IL USA Job Summary: This hybrid role is for a senior architect in data science who designs and delivers robust machine learning solutions using Python and exploratory data analysis for complex business problems. The role spans end to end architecture, from data ingestion to model deployment, ensuring scalable, secure and high quality systems that create measurable value for clients and society. Experience: 8 to 12 Years Required Skills: Python, Data Science, Machine Learning, EDA (Exploratory Data Analysis) Responsibilities: - Design scalable end to end machine learning architectures that transform complex business requirements into reliable data science solutions that deliver measurable value for clients and communities - Develop robust Python based pipelines for data ingestion, feature engineering and model training that ensure consistent accuracy, reliability and maintainability across projects - Perform deep exploratory data analysis on diverse data sources to uncover patterns, biases and quality issues that inform model design and data governance strategies - Define best practices for model lifecycle management including experimentation, versioning, monitoring and continuous improvement to maintain long term model health - Collaborate with cross functional teams in a hybrid work model to align machine learning architectures with product roadmaps, enterprise standards and regulatory expectations - Guide the integration of machine learning components into production systems by specifying interfaces, performance requirements and observability metrics for reliable operations - Optimize data science workloads for cost efficiency, scalability and resilience by selecting suitable tools, infrastructure patterns and optimization techniques for different use cases - Create clear technical documentation and architecture diagrams that enable engineers, analysts and stakeholders to understand, implement and evolve the data science platform efficiently - Establish quality gates and review processes for code, data and models that reduce defects, improve reproducibility and strengthen trust in analytical outcomes across the organization - Champion ethical and responsible use of machine learning by designing architectures that support transparency, fairness checks and explainability for critical decision workflows - Coordinate with security and compliance teams to embed data protection, access controls and audit capabilities within machine learning solutions while maintaining analytical flexibility - Mentor data scientists and engineers by reviewing solution designs, sharing architectural patterns and encouraging experimentation that aligns with organizational goals and customer impact - Measure and report the business impact of deployed models by defining key outcome metrics, feedback loops and learning mechanisms that continuously enhance products and services Qualifications: - Possess professional experience of eight to twelve years in data science or machine learning roles with significant responsibility for solution architecture and technical decision making - Demonstrate strong proficiency in Python including data manipulation, numerical computing and machine learning libraries applied to large scale production oriented projects - Exhibit extensive hands on experience in exploratory data analysis including data profiling, visualization and hypothesis testing to inform robust feature engineering and model choices - Show proven experience in designing and implementing supervised and unsupervised learning solutions including model evaluation, tuning and validation for diverse industry domains - Display familiarity with modern data platforms such as distributed storage, stream processing or orchestration tools that support scalable and resilient data science architectures - Apply sound understanding of software engineering practices such as version control testing documentation and continuous integration within data science workflows - Communicate complex analytical and architectural concepts clearly to both technical and non technical audiences enabling shared understanding and confident decision making - Maintain awareness of emerging trends in machine learning, data science tooling and architectural patterns and translate these developments into pragmatic improvements for the organization - Value inclusive collaboration in hybrid work settings by engaging thoughtfully in remote and in person interactions while maintaining high productivity and accountability Job Type: Business Associate Travel Required: No Certifications Required: N/A Sub Contractor Allowed: Yes
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