Srikanth Iyer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine Learning and Gen-AI | Leadership
As an Associate Director of Machine Learning in the Life Sciences Industry, I am passionate about leveraging the power of data and cutting-edge technologies to drive innovation and solve complex problems. With a strong background in machine learning, artificial intelligence, and data analytics, I have a proven track record of developing and implementing data-driven strategies that lead to business growth and enhanced operational efficiency. In addition to my technical skills, I am also highly proficient in effective communication and collaboration. I am skilled in articulating complex technical concepts in a way that is easily understandable to non-technical stakeholders. I am also a natural collaborator who thrives in cross-functional teams, working towards a shared goal. Throughout my career, I have worked on a diverse range of projects. This breadth of experience has given me a deep understanding of the various applications of AI not only in the life sciences industry but also in the various domains such as Healthcare, Insurance, Banking, Retail, Hospitality, Creative AI and sports. If you're looking for an experienced and driven professional who can effectively communicate and collaborate with cross-functional teams to achieve business objectives, I invite you to connect with me on LinkedIn. Let's explore how we can work together to make a positive impact on the world through the power of AI.
Indiana University Bloomington
Master of Science (MS), Computer Science
January 1, 2012 – January 1, 2013
Shanmugha Arts, Science, Technology and Research Academy
B.Tech, Computer Science Engineering
January 1, 2008 – January 1, 2012
St. Patrick's Junior College, Hyderabad
Intermediate I & II, Mathematics, Physics, Chemistry
January 1, 2006 – January 1, 2008
Bhavans Vidya Mandir (Elemekkara),Kochi
Middle School
January 1, 2002 – January 1, 2005
Coursera
Machine Learning ,Introduction to Recommender System ,NLP & Introduction to Marketing
N/A – Present
IQVIA
Associate Director
October 1, 2021 – Present
IQVIA
Sr Machine Learning Engineer
November 1, 2017 – October 1, 2021
BehaviorMatrix
Data Scientist
March 1, 2014 – August 1, 2017
Greater Philadelphia Area
Motorola Solutions
Data-Driven Research Intern at Chief Technology Office
May 1, 2013 – December 1, 2013
Greater Chicago Area
Cognizant Technology Solutions
Research Intern
December 1, 2011 – May 1, 2012
Concept Labs, IITM Research Park
Defence Research and Development Laboratory
Research Assistant
July 1, 2011 – December 1, 2011
SASTRA University
MindHelix Technologies
Application Developer Intern
June 1, 2011 – July 1, 2011
Kochi
Dijital Financial Advisor
Developer (Independent Project)
January 1, 2011 – February 1, 2011
National Institute of Ocean Technology
Research Assistant
June 1, 2010 – May 1, 2011
SASTRA University
Comparative Study of Classification & Regression Models for Sentiment Analysis on Recipe Reviews
November 1, 2013 – December 1, 2013
• Data Set consisted of 26,765 Food Recipes & 5,71,267 Recipe Reviews • Compared between SVM Classification (One Vs All) and SVM Regression. • Usage of Linear Kernel of SVM for was found to give better results as compared to the ones with Gaussian/Poly Kernel. • Feature selection was performed based on the TFIDF-Score of the terms(1-gram and 2-gram) in reviews. • Results for SVM Regression indicate that it is more precise in predicting Strong sentiments. • The F-Score for SVM Classification result is typically higher than that of SVM Regression. • RMSE value of SVM Regression model is typically lesser than that of SVM Classification.
Prediction of Web Page's Characteristic as Evergreen or Ephemeral
September 1, 2013 – October 1, 2013
•Terminology: -Evergreen web pages refer to those web pages that are active throughout. -Ephemeral web pages attract online traffic only for a short period of time. •.Used the dataset provided by stumbleUpon.com • Examined the results building a Decision Tree (C5.0 Algorithm) to classify the web pages based on various parameters of the web page. • Improved over the results significantly using TFIDF approach on the boilerplate text performing Logistic Regression over words as features. • Obtained an accuracy of 87.8%
Data Prediction of Admission to College for applicants using Probabilistic Approach
April 1, 2013 – Present
• Given a data set of various binary valued parameters like Personality,Difficu;ty of Curriculum, Intelligence, GPA, SAT,Writing Sample,Letterof Recommendation, Admission to College • For unobserved variables in the training data performed Variable Elimination to obtain inference on missing variables in the Bayesian Network • Obtained a converged system by using Expectation Maximization Algorithm and then used Maximum Likelihood Estimation to predict the value of Admission to College, given the values of other parameters. • Obtained an accuracy of 74.7%
Sentiment Analysis of Documents using Local Sentiment Flow
April 1, 2013 – Present
•Examined the problem of sentiment analysis of documents at various levels. •Objective of the system is to make use of sentiments at granular level to infer the sentiments at a higher level. •Global Sentiments are function of Local Sentiments (Sentiments of words for predicting sentiments of sentences and that of sentences to predict the sentiment of the documents) •The curve for most likely(probable) positive sentiments and negative sentiments are used as the evaluation parameter. •Used Hidden Markov Model's Forward-Backward Algorithm to obtain a smooth curve on the obtained sequence of sentiments of sentence to predict the global sentiment.
Face Recognition System using Markov Model
March 1, 2013 – Present
•Detects if a given image contains face and specifies its position. •Used Max-Product Belief Propagation Algorithm to recognize face with the training data containing positions of the features.
Study on Popularity Impact of Social-Media's Trending Topics on Real-World & Vice-Versa
January 1, 2013 – May 1, 2013
• Twitter as the representation of Social Media & Wikipedia as the real-life event's source • Comparing the popularity of trending topics in Twitter and hourly viewership of those topics in Wikipedia. • Studied on 12 Real-Life events that were trending between 11th Dec,2012 to 31st Dec,2012. • Performed analysis based on Graph Distribution,Outliers & Peak Detection of Page-Views/ Hashtags of the topic • Worked on various Peak Detection Algorithms based on hours of the day, days of the week and weeks of the month. • Concluded from the peaks,whether a particular topic’s popularity rose in twitter because of its viewership in Wikipedia or vice-versa, or becuase of any exogenous source using machine learning techniques.
Detection of Objects Approaching Ego-Centric Camera
November 1, 2012 – Present
• Constructed by making use of Optical Flow (Lucas Kanade Algorithm) of objects in the frame-sequence. • Pre-processed with Background Subtraction using Approximate Median method. • Evaluated from the distribution of the velocity vectors in the octants of plane geometry. • Obtained an Accuracy of 58.33% & Precision of 0.6
Object Recognition System
October 1, 2012 – October 1, 2012
• Built using the Histogram Intersection Method on the COIL-dataset (Columbia Object Library) • For making the system light-invariant the images were pre-processed with Color-Constancy Algorithm.
Face Classification System
September 1, 2012 – Present
•The system classifies a face in a given image into its gender(M/F) and expression (Happy/Neutral/Sad). •Trained using the best two Principal Components that the K-Mean Clustering method grouped data points into the desired classification categories.
Online Movie Ticket Booking System
August 1, 2012 – November 1, 2012
Developed Movie Ticket Booking system which can help users book tickets for one or more persons at a time and also select from available seats. The project composed of Admin, Manager and User modules. Language used : PHP, HTML, CSS
Management Fundamentals
University of Pennsylvania
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Drug Commercialization
Coursera
June 24, 2026 – Present
Docker for Data Scientists
June 24, 2026 – Present
Learning Amazon SageMaker
June 24, 2026 – Present
The Business of Health Care Specialization
University of Pennsylvania
June 24, 2026 – Present
Health Care Innovation
University of Pennsylvania
June 24, 2026 – Present
The Economics of Health Care Delivery
University of Pennsylvania
June 24, 2026 – Present
Developing Musicianship
Coursera
June 24, 2026 – Present
Introduction to Marketing
Coursera
June 24, 2026 – Present
Introduction to Machine Learning in Production
Coursera
June 24, 2026 – Present
Machine Learning Modeling Pipelines In Production
Coursera
June 24, 2026 – Present
Privacy Law and HIPAA
University of Pennsylvania
June 24, 2026 – Present
Introduction To Recommender Systems
Coursera Course Certificates
June 24, 2026 – Present
Introduction to Financial Accounting
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Machine Learning Data Lifecycle in Production
Coursera
June 24, 2026 – Present
Reinforcement Learning Foundations
June 24, 2026 – Present
Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Introduction to Music Production
Coursera
June 24, 2026 – Present
Introduction to Operations Management
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
The Data Science of Healthcare, Medicine, and Public Health, with Barton Poulson
June 24, 2026 – Present
Financial Acumen for Non-Financial Managers
University of Pennsylvania
June 24, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit through a diverse project portfolio, ranging from academic research to industry applications in various domains (e.g., healthcare, social media, civic data). The progression from individual contributor to leadership roles at IQVIA, coupled with continuous learning through numerous certifications, indicates a drive for growth, adaptability, and a commitment to staying current with industry trends. The focus on impactful projects and team leadership suggests a collaborative and results-oriented mindset.
Soft Skills & Operational Fit
The candidate's experience as an Associate Director and Sr Machine Learning Engineer at IQVIA highlights strong leadership, team management, stakeholder collaboration, and communication skills. The descriptions of managing product roadmaps and contributing to sales enablement suggest a business-oriented mindset and ability to align technical work with organizational objectives. The emphasis on continuous improvement and mentoring indicates a proactive and growth-oriented operational fit.