ANKUSH SINGAL
DATA SCIENTIST/MACHINE LEARNING SCIENTIST
Data Science │ Python │ Big Data
Location: Haryana, India │ ☎: - │ Nationality: Indian │DoB: Nov 13,1981
✉:-│ LinkedIn: https://www.linkedin.com/in/ankushsingal
Profile Snapshot
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Education
Passionate Machine Learning Scientist: A dedicated professional with over 6 years of
experience in the field of machine learning, specializing in areas such as Deep Learning,
Natural Language Processing (NLP), and Time Series Analysis.
AI Solutions Expertise: Proven track record of designing and implementing AI solutions
across various industries, including healthcare and FinTech, to solve complex business
challenges effectively and efficiently.
Technical Leadership: Adept at providing technical leadership, developing robust strategies
for enterprise solution deployment, and effectively communicating complex ideas to
top-level executives.
Training and Mentorship: Committed to knowledge sharing and fostering a culture of
continuous learning by providing mentorship and training to emerging talents in the field of
machine learning.
Collaborative Team Player: Skilled in establishing effective working relationships with
individuals at all levels of an organization, promoting a collaborative and data-driven culture.
Hands-on Implementation: Proficient in hands-on implementation of various AI/ML tasks,
including text clustering, similarity analysis, session analytics, and web activity analytics.
Innovation Enthusiast: Passionate about staying at the forefront of emerging quantitative
fields where data science and machine learning can play a significant role.
Open to Collaboration: Always open to new opportunities, collaborations, and discussions in
the exciting and ever-evolving field of machine learning.
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Master of Science in Electrical
Engineering
New Jersey Institute of
Technology, Jersey City, NJ
Year: 2006, GPA: 3.0
Bachelor of Electrical
Engineering
S.K. Institute of Engineering,
Kurukshetra University, Haryana
Year: 2003, GPA: 3.6
Professional Development
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NVIDIA Deep Learning
Conference, San Jose, CA
2016
DataWorks Conference, San
Jose, CA, 2018
Key Skills
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Data Science & Business Analytics
Large Language Models (LangChain)
Prompt Engineering
Artificial Intelligence
Solution Architecture
Data Manipulation and Analysis
Algorithm Development
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Production Model Deployment
Innovation Culture Cultivation
Process Improvement
Cognitive Computing
Machine Learning Insights
Customer Segmentation
Natural Language Processing (NLP)
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Text Mining
Linear Regression
Big Data Analytics
Team Management
Procedure Development
Service Standards
Support Ticket Forecasting
Work History
Organization
Designation
Duration
AI Company
Director of Data Science
Mar 2023 – Present
Foundation AI
Data Science Manager
Oct 2021 – Mar 2022
Booz Allen Hamilton
Data Science Manager
Oct 2018 – Jul 2021
Artificial Intelligence SME, Cambridge International Systems
Senior Solution Architect
Mar 2018 – Oct 2018
World Wide Technology
Big Data Architect
Mar 2017 – Mar 2018
U.S. Patent and Trademark Office, Alexandria, VA
Big Data Architect
May 2006 – Mar 2017
Job Responsibilities
AI Company │ Director of Data Science
● Collaborate closely with senior management to formulate a comprehensive strategy and approach for identifying and addressing
business challenges through the application of business science.
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Proactively recruit and nurture new talent, fostering a friendly and collaborative work environment while also cultivating the
development of future managers and leaders within the organization.
Cultivate and maintain effective working relationships across all organizational levels, including staff and management.
Develop and implement strategies to assist clients in harnessing the power of analytics, promoting a more data-driven culture within
their organizations.
Offer thought leadership in emerging quantitative fields where data science can make a significant impact.
Provide technical mentorship and guidance to data scientists, facilitating their professional growth and fostering innovative technical
thinking.
Lead hands-on implementation of various AI/ML tasks, including text clustering (such as KMeans and KMeans + UMAP), title similarity
analysis, session analytics, web activity analytics, and future title prediction.
Foundation AI │ Data Science Manager
● Oversee a team of 35 full-time equivalent (FTE) developers tasked with constructing an Extract Text platform through the utilization of
Natural Language Processing (NLP) and Computer Vision technologies.
● Collaborate closely with the Data Office and Technology teams to prioritize projects and ensure effective data oversight.
● Take a leadership role in motivating and managing employees, focusing on retaining high-performing talent while emphasizing an
inclusive and diverse perspective within the enterprise.
Booz Allen Hamilton │ Data Science Manager
● Led and directed a team of 30 full-time equivalent (FTE) developers in the creation of an open data platform using Databricks and
Swaggerhub, hosted on AWS. The project involved the implementation of various transfer-learning techniques, leveraging pre-trained
word embeddings such as Glove and BERT to construct the BERT search engine.
● Collaborated with stakeholders at multiple organizational levels to conceptualize, develop, and operationalize Key Performance
Indicators (KPIs) and Key Risk Indicators (KRIs), catering to both tactical and strategic business needs.
● Oversaw the team responsible for migrating Compliance data to the Cloud, specifically the Google Cloud Platform (GCP). Spearheaded
in N16, successfully transitioned to the Cloud environment, enabling enhanced Reporting and Analytics capabilities.
● Worked closely with the Data Office and Technology teams to prioritize projects and ensure effective data governance and oversight.
● Took on a leadership role in motivating and managing the team, with a focus on retaining high-performing talent while fostering an
enterprise-wide commitment to diversity and inclusion.
Artificial Intelligence SME, Cambridge International Systems │ Senior Solution Architect
● Provided valuable mentorship to the Digital Vanguard team, imparting expertise in all facets of Big Data and Machine Learning/Artificial
Learning (ML/AL).
● Held the responsibility of recommending suitable systems and tools for specific projects based on customer requirements and
resource availability. Over the past 5 years, deeply involved in Hadoop solutions for the Patent and Trademark Office, playing a pivotal
role in establishing and managing multi-node clusters.
● Actively participated in the development process by crafting user stories and prioritizing them as a product owner for the development
team.
● Conducted demonstrations of proof-of-concept software to various stakeholders, including the European Patent Office (EPO), US
Patent and Trademark Office (USPTO) management, and potential users in law firms.
● Took charge of researching the potential application of machine learning in patent claims processing, contributing to innovative
solutions in the field.
● Reviewed user stories for clarity and accuracy, providing recommendations to management regarding their priority.
● Led planning, design, implementation, and evaluation efforts within the functions to align with USPTO and user community objectives,
agency priorities, and anticipated trends.
● Served as an Analytic Program Advisor, offering guidance to Senior Executives on end-to-end program management aspects. This
included activities such as vendor selection, package selection, budgeting, work planning, staffing, change control, business
transformation, and training.
World Wide Technology │ Big Data Architect
● As a Senior Solution Architect, specialized in designing and implementing Big Data and Analytics solutions built upon the Hadoop
framework.
● Engaged in projects involving Hadoop and Artificial Intelligence while working with World Wide Technology. My project portfolio
included collaborations with notable clients such as GDIT, Statefarm, Lowe's, Mercy Hospital, PG&E, and JPMC.
● My responsibilities included the establishment of the Spark Environment at GDIT, enabling query execution on Hive within the Spark
and Impala Platform for a robust 128-node cluster.
● Additionally, played a crucial role in configuring the Cloudera Data Science Workbench (CDSW) with Docker/Kubernetes to facilitate
benchmarking tasks using Python.
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Also set up the Nvidia environment for conducting benchmarks utilizing TensorFlow with a Convolutional Neural Network (CNN)
model, leveraging ImageNet Data across Dell, IBM, and Nvidia DGX-1 platforms.
