Moid Hassan

AI Researcher

Delhi, India9 yrs 7 mos experience

AI EnabledHighly Stable

Key Highlights

10+ years of experience in machine learning systems.
Expertise in NLP and unstructured data processing.
Proven track record in predictive modeling and feature engineering.

Stackforce AI infers this person is a Data Scientist specializing in Fintech and SaaS with strong expertise in machine learning and NLP.

Contact

Skills

Core Skills

Natural Language Processing (nlp)Recommender SystemsDeep LearningMicrosoft Azure

Other Skills

Agentic AI DevelopmentForecastingMachine LearningPythonDistributed SystemsStatistical ModelingData AnalysisData MiningData VisualizationBusiness AnalyticsPySparkHadooppandasSQLMicrosoft Excel

About

Data Scientist with 10+ years of experience at Microsoft and American Express, building production-grade machine learning systems at scale. Experienced in owning the end-to-end ML lifecycle, from experimentation and data pipelines to deployment in real-world environments. Extensive experience in NLP, with multiple years of building and deploying systems for unstructured data processing. Over the past 1–2 years, have designed and deployed agent-driven systems enabling automated workflows on top of large language models. Mentoring interns and data scientists for the last 3 years.

Experience

9 yrs 7 mos

Total Experience

5 yrs 2 mos

Average Tenure

4 yrs 5 mos

Current Experience

Microsoft

2 roles

Data and Applied Scientist II

Sep 2022 – Present · 3 yrs 8 mos · Noida, Uttar Pradesh, India

Enhancing the Microsoft Dynamics 365 Sales with AI Agents
Powering the M365 Feeds experience with Weblinks and News Recommendations, Microsoft Search and Intelligence

Natural Language Processing (NLP)Recommender Systems

Data Scientist II

Dec 2021 – Sep 2022 · 9 mos · Noida, Uttar Pradesh, India

Deep LearningMicrosoft Azure

American express

2 roles

Data Scientist II

Jan 2020 – Dec 2021 · 1 yr 11 mos

Developed various categories of predictive models (qualification, credit card propensity, uplift, interest-based models) for prospect acquisition for proprietary cards via digital channels using GBM and XGBoost; e.g., Credit Worthiness model to target approvable prospects which led to 10%+ improvement in approval rates in Media channel. The performance of these campaigns is measured by A/B testing. Collaborated with marketing, analytics and platform teams to design, implement and measure multiple campaigns.
Performed a deep dive analysis of internal and external data sources (bureau. demographic, spend, firmographic, mobile, social) to enhance feature engineering, capturing intelligence for use in acquisition modeling.
Developed and automated the Media Mix Modeling (MMM) methodology which measures the impact of various marketing activities on different outcomes like conversions, site visits, clicks, etc. It is further used to optimize the advertising mix with respect to these different outcomes.

Data Scientist, Amex Advance

Sep 2016 – Dec 2019 · 3 yrs 3 mos

Worked on implementing an automated and distributed Feature Engineering pipeline that can ingest over 10k+ raw features & billions of transactions and gives out ready to use custom features in multiple models.
Created a rule-based Data Schema Inference algorithm which assigns a data type (numerical, categorical, date, ID) by looking at the variable values. The algorithm is implemented in Hadoop Streaming (Map-Reduce) making it distributed and scalable.
Built predictive models using ML algorithms to generate practical insights and target lists for clients/advertisers like Verizon, Bridgevine, Samsung, Singapore Airlines etc., using AXP closed loop data and purchase-based signals, enhanced by second- and third-party data. Aided in the generation of $12MM+ revenue for Amex.
Developed a Web Page categorization capability leveraging 100MM+ web pages to retrieve useful structured information based on page content analysis.