Blend360

Data Scientist

• Define technical investments with business objectives
• Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations and a high-performance culture.
• Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact.
• Establish engineering standards, code review practices, and model governance frameworks across the AI org.
• Serve as the technical authority on deep learning architecture—personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction.
• Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom.
• Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance.
• Champion reproducible experimentation and architectural decision documentation across the team.
• Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment.
• Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks.
• Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs.
• Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives.
• Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence.
• Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice.
• Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
• Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC).
• Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements.

Director, AI Engineering – Data Science

QA Engineer

• Ensure data quality, reliability, and consistency across modern data platforms.
• Validate data pipelines and implement automated quality checks.
• Collaborate closely with Data Engineering and business teams to guarantee accurate and production-ready data assets.
• Design and implement a data quality framework across Bronze, Silver, and Gold layers.
• Build and maintain automated data quality checks within Databricks pipelines.
• Own reconciliation between source systems and Databricks layers.
• Validate identity resolution outputs in the Silver layer.
• Perform end-to-end pipeline testing.
• Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables.
• Support UAT with client business stakeholders.
• Document all QA processes, test results, and data quality findings.
• Monitor pipeline health post-deployment.

QA Analyst, Databricks

• Design and implement a data quality framework across Bronze, Silver, and Gold layers — defining validation rules, threshold tolerances, and alerting standards.
• Build and maintain automated data quality checks within Databricks pipelines — row counts, null checks, referential integrity, schema validation, and business rule assertions.
• Own reconciliation between source systems and Databricks layers — ensuring source data lands accurately and transformations produce expected outputs.
• Validate identity resolution outputs in the Silver layer — reviewing match rates, investigating false positives and false negatives, and ensuring enterprise identifiers are being assigned correctly across source populations.
• Perform end-to-end pipeline testing — validating that data flows correctly from ingestion through to the Gold layer and that downstream reporting outputs reflect accurate data.
• Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables before they are promoted to production.
• Support UAT with client business stakeholders — helping them validate that Gold layer outputs meet their reporting requirements.
• Document all QA processes, test results, and data quality findings in a format that can be handed off to the client team at engagement close.
• Monitor pipeline health post-deployment — investigating and triaging data quality incidents and working with engineers to resolve root causes quickly.

• Ensure data quality, reliability, and consistency across modern data platforms.
• Validate data pipelines and implement automated quality checks.
• Collaborate closely with Data Engineering and business teams.
• Design and implement a data quality framework across Bronze, Silver, and Gold layers.
• Build and maintain automated data quality checks within Databricks pipelines.
• Own reconciliation between source systems and Databricks layers.
• Validate identity resolution outputs in the Silver layer.
• Perform end-to-end pipeline testing.
• Partner with Data Engineers to define acceptance criteria for each sprint.
• Support UAT with client business stakeholders.
• Document all QA processes, test results, and data quality findings.
• Monitor pipeline health post-deployment.

Manager

• Manage multiple projects within a B2B portfolio
• Providing efficient and effective coordination of complex activities
• Clearly defining project team and stakeholder roles and responsibilities
• Ensuring effective identification of risks and proactive solutioning
• Responsible for strategically growing a B2B portfolio
• Must have B2B product suite knowledge
• Strong skills in account maintenance, information gathering, training, pricing, sales strategy, and selling

Commercial Card Supplier Enablement Manager

• Architect, train, and deploy generative AI models, including large language models (LLMs) and transformer-based systems.
• Develop robust predictive and optimization models using structured and unstructured data sources.
• Implement and deploy machine learning models using AWS SageMaker for scalable and efficient performance in a cloud environment.
• Partner with cross-functional teams to translate business requirements into scalable AI solutions.
• Drive model lifecycle management from research and experimentation through deployment and monitoring.
• Maintain high standards for model governance, explainability, performance, and ethical AI.
• Stay at the forefront of GenAI advancements and proactively evaluate new tools, methods, and frameworks.

Senior Data Scientist – GenAI

Business Intelligence Developer

• The Senior BI Analyst will play a key role in transforming complex data into actionable insights that drive strategic business decisions.
• This role focuses on developing, optimizing, and managing internal dashboards and reports that track key business metrics and operational performance.
• This person will collaborate closely with internal teams—such as Finance, Operations, People Analytics, and Delivery—to ensure high-quality insights and data accuracy across the organization.
• Design, build, and maintain internal BI dashboards and reports to monitor company KPIs and operational performance.
• Collaborate with cross-functional teams to gather business requirements and translate them into effective BI solutions.
• Develop and optimize data models and ETL processes to ensure data consistency and accuracy.
• Present insights and recommendations to senior leadership, supporting strategic and operational decisions.
• Implement best practices in BI design, data governance, and process automation.
• Support internal initiatives by providing analytical expertise and data visualization capabilities.
• Manage multiple internal BI projects end-to-end, ensuring timely delivery and stakeholder satisfaction.

Lead Power BI Analyst

Product Designer

• Lead end-to-end design processes, from discovery and user flows to high-fidelity prototypes and developer-ready assets.
• Collaborate with product managers, data scientists, and engineers to solve complex, data-driven challenges.
• Translate user research, business requirements, and technical constraints into intuitive and accessible interfaces.
• Design and iterate on AI-powered product experiences, applying human-centered design principles.
• Ensure high standards of visual design, interaction design, and usability across all deliverables.
• Contribute to and evolve design systems, ensuring consistency and scalability.
• Participate in client-facing sessions, presenting design solutions and incorporating feedback.
• Advocate for user experience best practices throughout the product lifecycle.

Senior Product Designer

Implementation Specialist

• Provide high-quality day-to-day operational and technical support to financial institution partners and internal stakeholders.
• Act as a liaison between clients and internal product, engineering, and support teams to ensure issues are resolved efficiently.
• Manage and coordinate support tickets, work orders, and case resolution activities across commercial payment platforms.
• Analyze and troubleshoot processing issues, identifying root causes and communicating impact, resolution steps, and timelines.
• Proactively identify processing inefficiencies and improvement opportunities and recommend solutions to enhance performance.
• Monitor and communicate service levels (SLAs), providing status updates and escalation management when needed.
• Develop and maintain support documentation, knowledge articles, and process guides.
• Partner with account and technical teams to ensure client needs are met and expectations are exceeded.
• Prepare and deliver client-ready updates and presentations using data insights and performance reporting.
• Escalate critical issues appropriately and provide leadership in driving resolution.

Implementation Consultant

Client Partner

• Leads and owns the account strategy, growth execution, and overarching P&L.
• Identifies new business opportunities with existing client portfolio, pitches Blend360’s services and capabilities successfully to achieve closed deals; this includes writing proposals and Statements of Work (SOW).
• Negotiates best possible pricing to achieve the most optimal value for the client.
• Manages pipeline to include all leads, opportunities, notes and activities in a timely manner.
• Responsible for overall client satisfaction.
• Facilitates getting the right Blend360 talent, skills and offerings to the decision makers at the client.
• Serves as a strategic AI and transformation advisor to client stakeholders, helping shape and guide AI strategy, data modernization, and technology-enabled business transformation initiatives aligned to client long-term goals.
• Partners with and aligns internal Blend360 practice and capability leaders to orchestrate a cohesive, forward-looking approach to each client engagement, ensuring delivery excellence and long-term relationship health.

Client Partner – Hospitality, Travel, Retail

Analyst

• Develop a strong understanding of internal and external data flows supporting business initiatives
• Identify, access, and connect to data sources including data warehouses and third-party platforms
• Evaluate existing tools, systems, and platforms used to support analytics and reporting
• Analyze current business rules, logic, and system configurations to identify inefficiencies or opportunities for improvement
• Map data movement across systems throughout the customer/account lifecycle
• Execute data queries to validate accuracy, completeness, and integrity of datasets
• Analyze performance metrics to measure success of initiatives and validate expected outcomes
• Monitor portfolio health indicators including growth, profitability, and credit performance
• Develop and maintain KPI reporting across key business areas such as marketing, underwriting, and account management
• Build forecasting models to evaluate the financial impact of proposed solutions
• Establish reporting cadence and define metric ownership across stakeholders
• Create, execute, and validate recurring reports, ensuring alignment with business and system records
• Perform ad hoc analysis to uncover trends, behaviors, and performance drivers
• Develop presentation materials to communicate insights and recommendations to business stakeholders
• Document reporting logic, data sources, and methodologies to ensure transparency and repeatability
• Create and maintain a comprehensive data dictionary to standardize definitions and usage across teams

Credit Analyst

Data Engineer

• Design, build, and maintain scalable ETL/ELT pipelines using Python and SQL on modern lakehouse architectures.
• Develop data solutions using tools such as Azure Data Factory, Synapse, Fabric, Airflow, or dbt.
• Build and maintain data models and pipelines that support forecasting and customer retention/churn use cases, including feature engineering and reproducibility.
• Integrate data from multiple transactional systems (ERP, CRM, etc.), ensuring proper standardization, entity resolution, and consistency across datasets.
• Implement data quality frameworks, validation processes, and monitoring to ensure reliability and accuracy of data pipelines.
• Design dimensional and semantic data models to support BI and analytics consumption, enabling drill-down capabilities from executive dashboards to detailed views.
• Collaborate with analytics and business teams to support reporting, forecasting, and AI-driven initiatives.
• Ensure data solutions are scalable, performant, and aligned with best practices in data architecture.

Lead Data Engineer

• Advanced AI Solution Design & Architecture: Lead the technical design, development, and architecture of cutting-edge AI and Generative AI solutions. This includes selecting appropriate models (LLMs, diffusion models, etc.), designing data pipelines for AI training and inference, and architecting scalable and robust AI systems.
• Strategic Client Engagement & Technical Advisory: Serve as a trusted technical advisor to national and international clients, guiding them from strategic planning stages through to the pilot and full-scale implementation of complex AI and GenAI projects. Confidently present and articulate Blend360's technical capabilities and the strategic implications of AI solutions to senior client stakeholders.
• Technical Implementation & Oversight: Provide hands-on technical leadership and guidance throughout the entire project lifecycle, from proof-of-concept to pilot and full-scale production deployment. Ensure technical excellence, adherence to best practices, and the delivery of high-quality, performant AI solutions.
• Thought Leadership & Innovation: Drive thought leadership in the evolving landscape of GenAI, large language models (LLMs), agentic AI systems, and emerging AI techniques. Research and evaluate new technologies, contribute to internal IP development, and publish insights that position Blend360 at the forefront of AI innovation.
• Business Development Support & Technical Scoping: Actively participate in presales activities, providing deep technical expertise for proposal development, solution architecture, and technical scoping. Support the identification and capitalization on new business opportunities by demonstrating our technical capabilities and value proposition.
• AI Strategy & Roadmapping: Collaborate with clients to understand their business challenges and translate them into actionable AI strategies and technical roadmaps. Develop bespoke GenAI solutions, facilitate seamless integration, and advise on organizational changes necessary for AI adoption.
• Project Leadership & Execution: Lead and manage specific technical workstreams within complex projects, ensuring deliverables meet the strategic goals, technical excellence, and quality standards expected by our clients. Drive cross-functional collaboration with data scientists, machine learning engineers, and other specialists to ensure successful technical outcomes.
• Service Evolution & Go-to-Market: Contribute to the continuous development and technical refinement of Blend360's AI and Generative Solutions offerings. Support go-to-market strategies by providing technical insights and expertise for effective positioning in the market.

Director, AI Engineering – Data Science

QA Analyst, Databricks

QA Analyst, Databricks

QA Analyst, Databricks

Commercial Card Supplier Enablement Manager

Senior Data Scientist – GenAI

Lead Power BI Analyst

Senior Product Designer

Implementation Consultant

Client Partner – Hospitality, Travel, Retail

Credit Analyst

Lead Data Engineer

Senior Director – AI Engineering, Data Science