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I am a Data Scientist and ML Engineer with 3+ years of experience building production data pipelines, supporting ML training and inference, and delivering experimentation insights at scale.\n\nI am proficient in Python, SQL, and Spark, with hands-on experience improving data quality, feature performance, and model reliability across large transactional datasets. I translate ambiguous business problems into measurable metrics and actionable decisions through close collaboration with product and engineering teams.…I am a Data Scientist and ML Engineer with 3+ years of experience building production data pipelines, supporting ML training and inference, and delivering experimentation insights at scale.\n\nI am proficient in Python, SQL, and Spark, with hands-on experience improving data quality, feature performance, and model reliability across large transactional datasets. I translate ambiguous business problems into measurable metrics and actionable decisions through close collaboration with product and engineering teams.

Mohammad Aszad Syed

Data Scientist, AI Engineer, Data Analyst, +2

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I am a Data Scientist and ML Engineer with 3+ years of experience building production data pipelines, supporting ML training and inference, and delivering experimentation insights at scale.\n\nI am proficient in Python, SQL, and Spark, with hands-on experience improving data quality, feature performance, and model reliability across large transactional datasets. I translate ambiguous business problems into measurable metrics and actionable decisions through close collaboration with product and engineering teams.…I am a Data Scientist and ML Engineer with 3+ years of experience building production data pipelines, supporting ML training and inference, and delivering experimentation insights at scale.\n\nI am proficient in Python, SQL, and Spark, with hands-on experience improving data quality, feature performance, and model reliability across large transactional datasets. I translate ambiguous business problems into measurable metrics and actionable decisions through close collaboration with product and engineering teams.

Available to hire

I am a Data Scientist and ML Engineer with 3+ years of experience building production data pipelines, supporting ML training and inference, and delivering experimentation insights at scale.\n\nI am proficient in Python, SQL, and Spark, with hands-on experience improving data quality, feature performance, and model reliability across large transactional datasets. I translate ambiguous business problems into measurable metrics and actionable decisions through close collaboration with product and engineering teams.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

ML Engineer at Capital One Financial

January 1, 2025 - Present

Built production data pipelines using Python, SQL, and Spark to support ML training and inference, increasing usable model data availability by 28% across customer behavior and risk analytics workflows. Reduced data-related model failures by 35% by adding validation rules, schema checks, and freshness monitoring before datasets reached training and scoring stages. Improved feature generation performance by 22% through Spark optimization and efficient SQL joins on terabyte-scale cloud datasets. Converted one-off model inputs into reusable feature datasets, enabling multiple data science teams to run experiments faster with consistent data definitions. Decreased incident resolution time by 30% by adding monitoring signals and clear failure diagnostics for ML-critical pipelines.

Data Scientist, Product & Experimentation Analytics at Accenture

August 1, 2021 - July 1, 2023

Analyzed 50M+ weekly transaction records using advanced SQL and PySpark to support experimentation, forecasting, and fraud analysis used in pricing and risk decisions. Designed and evaluated A/B and multivariate experiments with proper power analysis and bias controls, contributing to 20%+ improvements in conversion and retention. Identified funnel drop-offs and cohort behavior patterns, leading to product and incentive changes that improved ROI by 15–22% within measured release cycles. Defined engagement and activation metrics where none existed, enabling consistent tracking of DAU, WAU, and MAU across product teams. Shortened decision turnaround by 25% by translating statistical findings into clear, actionable recommendations for product and engineering stakeholders.

Data Science Intern at Kaashiv Infotech

March 1, 2020 - August 1, 2020

Cleaned and structured 30K+ customer records using SQL, improving dataset reliability for churn and fraud analysis. Built a Python-based churn prediction model achieving ~85% accuracy, identifying behavioral risk indicators used in retention analysis. Reduced manual analysis effort by 60% by developing a lightweight dashboard to visualize churn trends and customer segments.