Data Science Machine Learning

Amitabha Karmakar

********.********@*****.***

Ph: 773-***-****

Summary

Innovative and strategic data science executive with a strong track record of partnering with senior business, product, and engineering leaders to define and execute on long-term strategic goals that enhance the company’s technology and analytics capabilities. Adept at translating high-level business objectives into actionable data science roadmaps that drive measurable impact across functions. Recognized technical leader within data science, with deep experience instituting engineering best practices—including robust coding standards, scalable architecture, and automated testing—while building the technical depth and agility of teams. Passionate about fostering a high-performance, inclusive, and collaborative team culture that promotes innovation and continuous learning. Instrumental in establishing data governance and best practices across the organization, playing a key role in cultivating a data-first culture. Drives alignment with cross-functional stakeholders to embed data science into core business decision-making processes, ensuring data is leveraged effectively to improve product development, customer experience, and operational efficiency. Proven expertise in conceiving, developing, and deploying advanced machine learning and AI solutions that solve complex problems and create competitive advantages. Demonstrated ability to develop new capabilities by applying statistical modeling, predictive analytics, and custom algorithms to real-world business challenges.

Adept at recruiting, mentoring, and developing data science talent at all levels, establishing clear mission and vision for the function, and building an environment that enables team members to grow and thrive. Provides thoughtful leadership and career development pathways, ensuring sustainable growth for both individuals and the organization. Skilled in collaborating across departments—product, engineering, finance, operations, and marketing—to build data-informed strategies, shape product capabilities, and measure success through well-defined metrics and analytical frameworks. Champions the democratization of data and analytical literacy, ensuring data science techniques are accessible and understandable to business audiences.

Leads the end-to-end execution of data science and analytics initiatives, from strategic planning to operational delivery, ensuring alignment with business priorities and maximizing ROI from data investments.

Education:

MS in Data Science, UC Berkeley

B Tech Computer Science, IIT Kanpur

Other Contributions

Patent: Cloud Provisioning Marketplace for Services Filed May 22, 2017 Patent issuer and number: us 62/509330

Machine Learning Research Cornell PrePub: https://arxiv.org/abs/1802.00382 Apache Cassandra committer: 2007 - 2010.

Built, trained and published Cloud Architect GPT: Cloud Architect Guide Built, trained and published Machine Learning Architect GPT: ML Architect Pro My Generative AI Newsletter: https://ai-this-week.beehiiv.com/ TECHNICAL SKILLS

PROFESSIONAL EXPERIENCE

Languages Java, Go, Python, C++, Scala, R, CUDA, NodeJs, Coffee, Ruby, Perl, PL/SQL, MATLAB, Kotlin, TypeScript

NoSQL DB Technologies Cassandra, Pig, Hadoop, MongoDB, Redis. Storage Systems HDFS, GPFS, SAN, ADLS, Blob, SAN, Cloud SAN, NFS. API design Event-driven/reactive, Streaming, Rest over HTTP, GraphQL, gRPC. Data Engineering Spark, Map-Reduce, Apache Flink, Kafka, Kappa framework, TAO, DBt, Clickhouse

Machine Learning DNN, OpenCV, TensorFlow, PyTorch, AutoML frameworks. Managed Notebooks AirBnB Redspot, Uber DSW, Kubeflow, Metaflow, MLFlow, Determined AI, Domino DataLabs, Paperspace Gradient. Model Training AirBnb BigQueue, Horovid, Kubeflow Fairness, TFX Data Labeling ScaleAI, Snorkel, LabelBox, SageMaker GroundTruth Workflow Orchestration Flyte, AirFlow, Argos, KubeFlow Pipelines, AWS DataPipelines Feature Store Zipline, Michaelangelo Palette, FBLearner, Techton,Scribble,Hopsworks

Model Management MLFlow, Metaflow, Tensorboard

Model Serving Nvidia Triton, KF Serving, TF Serving, Seldon Core, BentoML, MLRun, CML (Continuous Machine Learning)

Model Monitoring and

Explainability

SHAP, LIME, Captum, Fiddler, Arthur, Arize, EvidentlyAI, Kedro, ZenAML,

Cloud Technologies AWS, Azure, Mesos, CloudFoundry, Kubernetes Scripting Shell, Perl, Pig

AI Agents and Automation LangChain, Langraph, Model Context Protocol, N8N and other agent builder frameworks etc.

Material Bank, Vice President AI and Data Engineering February 2024 - Current Material Bank is a Series D, B2B e-commerce platform revolutionizing how architects and designers access building material samples, with rapid expansion across the U.S., EU, and Japan in partnership with SoftBank. Reporting to a founding executive from Amazon, I lead the Data Engineering, Machine Learning, Search, and Agentic AI teams, overseeing a globally distributed organization.

I have driven the strategic vision and execution for mission-critical systems that ensure site stability, sub-second latency, and scalable infrastructure. Under my leadership, we delivered transformative, customer-facing innovations including a dynamic Loyalty Program, Instant Checkout, Digital Sampling, and AI-powered agents that significantly elevated the user experience.

My team pioneered advanced discovery tools, such as Visual Search and Contextual Recommendations, and built an internal Multi-Channel Personalization (MCP) server to seamlessly integrate supply chain, advertising, and search functionalities. We developed next- generation Agent-Based Search capabilities comparable to Amazon’s Rufus, setting a new standard for intelligent product discovery in B2B e-commerce. In addition to product innovation, I have established best-in-class engineering and data science practices, mentored diverse technical talent, and fostered a high-impact culture focused on innovation, accountability, and measurable business outcomes. Head of East Coast Data Labs & Generative AI Program – Amazon Web Services (AWS) April 2022 - February 2024

As the Head of AWS’s East Coast Data Labs and Generative AI Program, I lead a cross- functional team of data science and architecture experts dedicated to helping organizations unlock transformative value from their data. My role spans strategic leadership, technical innovation, and organizational enablement in the areas of advanced analytics, machine learning, and generative AI.

I drive the development and deployment of sophisticated data science solutions tailored to real- world business problems across industries. These include large-scale forecasting systems, natural language understanding, image recognition, personalization engines, anomaly detection, and generative AI applications. I lead efforts to design and scale these solutions using AWS’s ecosystem—including SageMaker, Bedrock, Redshift, and other advanced services—ensuring performance, cost-efficiency, and ethical AI standards. My team partners directly with enterprise and startup data science teams to co-create robust, end- to-end pipelines—from data ingestion and feature engineering to model training, tuning, deployment, and lifecycle management (MLOps). We also provide strategic guidance on experimentation design, causal inference, and statistical modeling to support data-driven decision-making across product development, operations, and customer engagement. As a thought leader in the data science community, I contribute to internal and external knowledge dissemination by developing reusable architectures, publishing technical insights, and mentoring AWS teams and customer stakeholders on best practices. I foster a high-performance culture rooted in continuous learning, experimentation, and scientific rigor—positioning AWS as a premier partner in the era of AI transformation. Through this role, I help shape how data science is practiced at scale, translating cutting-edge research into practical solutions that deliver measurable business outcomes. American Express, Vice President AI and DataEngineering June 2021 – April 2022

At American Express, I lead the Enterprise Data, AI, and Cloud business units, with a core focus on harnessing data science and artificial intelligence to drive strategic growth and innovation across the organization. I oversee the development and execution of a comprehensive data and AI strategy that spans multicloud architecture, global expansion, and modernization of the enterprise data and ML ecosystem.

Under my leadership, the organization has expanded its AI and data engineering footprint while maintaining operational efficiency through smart infrastructure design and cost rationalization. My team plays a pivotal role in developing scalable machine learning solutions and intelligent systems that directly contribute to top-line revenue generation—enabling personalized customer experiences, intelligent risk modeling, and advanced decision support. We also design and implement end-to-end data pipelines and real-time monitoring frameworks that support critical business needs, including compliance and governance. A notable achievement includes the development of automated AI-powered monitoring and auditing tools for PCI DSS compliance, streamlining reporting to entities such as the Privacy Rights Clearinghouse.

I work at the intersection of data science, engineering, and cloud strategy, aligning technical roadmaps with business objectives while cultivating a culture of scientific rigor, transparency, and continuous innovation.

Peloton Interactive (VP of Data, AI and Data Engineering) Sept 2020 - June 2021

As Vice President of Data at Peloton, I led the global data organization, overseeing data science, analytics, and data platform engineering across a multi-hybrid cloud environment. My leadership spanned multiple geographies and adhered to key regulatory frameworks including GDPR and HIPAA, ensuring compliance, security, and responsible data use. I directed the design and deployment of scalable, secure data infrastructure and advanced analytics solutions that empowered all business units—from marketing and product to operations and finance

—to make informed, data-driven decisions. My teams applied predictive modeling, customer segmentation, and real-time analytics to set and forecast revenue targets, optimize user engagement, and inform strategic business moves.

Working cross-functionally, we played a key role in defining and executing growth strategies, leveraging data science to guide go-to-market planning and performance measurement for new regional launches. I also oversaw the implementation of machine learning systems that supported personalization, churn prediction, supply chain efficiency, and demand forecasting. Through this role, I championed a data-first culture at Peloton, embedding analytics into the core of business strategy and scaling the organization’s ability to make high-impact, data-informed decisions. Executive Director JP Morgan Chase Jan 2016 - Sept 2020 As the leader of J.P. Morgan’s Intelligent Solutions business unit, I defined and executed the global Data and AI strategy across all major lines of business, including Consumer & Credit Banking, Investment Banking, Wealth Management, Risk, and regional operations in China and South America. My role involved close collaboration with the C-suite to ensure data science and AI initiatives were aligned with strategic priorities and regulatory frameworks. I directed the development of enterprise-wide machine learning platforms and advanced analytics frameworks that powered high-impact use cases—from credit risk modeling and fraud detection to portfolio optimization and customer personalization. My leadership was pivotal in driving the adoption of data science as a core business enabler across the global organization. In addition, I established J.P. Morgan’s first FinTech incubation program, creating a bridge between cutting-edge startups and internal business units. I advised early-stage companies on meeting complex banking regulatory requirements (e.g., compliance, security, reporting) and structured pilot programs to evaluate and deploy their AI and data-driven solutions within the bank's diverse operating environments.

This role positioned me at the nexus of innovation, data strategy, and financial services transformation, enabling the firm to scale AI capabilities responsibly and competitively across global markets.

Apple Online Store Consulting March 2015 – Jan 2016 I architected and executed Apple’s global data and AI vision for the online store, deploying cutting-edge, distributed machine learning systems to power critical applications such as fraud detection, behavioral analysis, and personalized customer experiences. These scalable ML pipelines were designed for real-time inference and integrated seamlessly into a multi-cloud, containerized infrastructure—ensuring high availability, low latency, and global compliance. Key initiatives included:

• Machine Learning for Fraud Detection: Developed and deployed advanced ML models for fraud prevention, significantly reducing financial risk across transactions at scale.

• Security Strategy: Integrated AI-driven anomaly detection and blockchain technologies to strengthen cyber defense and transactional integrity.

• Multi-Cloud and Containerization Strategy: Led the design of cloud-agnostic containerization frameworks to enable secure and scalable deployments across AWS, GCP, and Apple’s internal cloud infrastructure.

• Geographic Expansion Enablement: Partnered with regional leadership to support localized rollouts using predictive analytics for market segmentation and performance forecasting.

My work fused innovation with operational discipline, laying the foundation for a data-driven, AI-powered commerce platform that could scale securely and intelligently across global markets. Director at Warner Music Group Oct 2013 - Mar 2015 Head of Engineering in charge of a budget exceeding $20M. I lead the engineering organization in building a Cloud Platform based on Cloud Foundry OSS, Java and Scala based Micro-services, and business facing responsive design web applications. Using the technology platform developed by my team, the company reduced the Capex and Opex for the global business by 100MM and increased the top line revenue by 40% with a stronger global reach out. Director of Technical Services, HBO July 2011 - Oct 2013 Designed and implemented the HBOGo, MaxGo and HBONow platforms. Feature implementations included subscription models, PPV (pay-per-view), player security, encryption, DRM etc. Built the platform to handle internet scale concurrent loads for peak consumption.

Technical Principle, ThoughtWorks October 2007 – July 2011 Established the Fin Tech field and GTM teams for Thoughtworks. We provided Solution Architect and Resident Architect services to Fortune 100 finance companies across the globe. I implemented the GTM mechanisms, sales cycles, growth forecasting methods to help create this new Business for Thoughtworks. We also provided presales and Sales for the products developed by Thoughtworks Studios.

Vice President Equity Trading Platform at Lehman Brothers June 2004 - October 2007

Designed and implemented equity research platform using concurrent programming. Developer at General Motors Components Holdings, LLC June 2002 - June 2004

Implemented an online order booking system for the bank which can support several concurrent customers across geographies.

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