About Me

This blog contains notes for myself from various published papers,journals,websites, books and blogs. It also contains some of the projects I completed and my thoughts and point of view of certain topics. I hope this blog is helpful to you.

Following is some information about me.

Summary

Artificial Intelligence (AI)/Machine Learning (ML) engineer with a proven record of innovation and ability to work within multi-disciplinary teams to deliver valuable insights via data-driven methods and communicate results to diverse audiences.

Experience 

Artificial Intelligence (AI) / Machine Learning (ML) Engineer ,  Optum (United Health Group)   |   Sept. 2018 to Present

  • Applied variety of machine learning/ deep learning algorithm to data sets  to optimize and improve customer experiences and business strategies in different domains such as financial, wearable or clinical.
  • Interacted and collaborated with cross-functional teams of size 2 – 15 to ensure complete delivery of solutions.
  • Generated actionable insights for business sponsors by finding, retrieving, cleaning and analyzing appropriate data to identify trends and patterns.
  • Participated in daily stand-up meetings, planning meetings and review sessions using Scrum /Agile methodology.
  • Mentored junior team members in their technical and professional development.

Technology Development Program Associate, Optum (United Health Group)   |   Sept. 2017 to Sept. 2018   

  • Gained experience to work creatively and analytically to address complex challenges.
  • Semi-finalist in a global, company-wide BizViz Data visualization challenge (among 300,000 employees) to demonstrate the business challenges in an innovative manner to the senior leadership.
  • Participated in various corporate social responsibility events both inside and outside the organisation to make an impact on the society e.g., won a hackathon in collaboration with Google and Angel hack to find smart solutions to Dublin’s challenges.
  • Gained knowledge and good hands-on experience of working with big data technologies e.g., Spark and cloud environments e.g., Azure, Kubernetes.
  • Successfully completed online courses in different subjects such as AI, NLP, statistics, data warehousing and deep reinforcement learning complement to gain theoretical knowledge and qualification.

Data Scientist/ Data Analyst, Self Employed   |   July 2013 to Sept. 2017

  • Worked independently as well as in team environment for various hackathons (e.g., Insure tech Hackathon at NDRC ), volunteering (e.g., Data Kind), Kaggle competitions (e.g., predicting Red Hat business value) and personal projects.
  • Applied machine learning and data analytics to solve real business problems such as maximize revenue for direct marketing campaigns, predict number of passengers processed at 15 – minute interval in a day at Dublin Airport.
  • Designed, installed, tested maintained and search engine optimized a blog at https://adataanalyst.com
  • Developed an Enterprise Resource platform solution for a large multinational insurance provider with 3 other classmates.

Awards

  • Winner @Google Dublin and Smart Dublin’s #SmartCity challenge
  • 1st prize for solution using APIs to address the challenges around leisure or business travel.
  • Special Recognition Prize to help make Dublin more inclusive to people with mobility problems.
  • 2nd Prize for solving the challenge to predict how many passengers will need to be processed at each fifteen-minute interval in the day.

Skills

  • Machine Learning: Statistical Modelling (e.g., Regression, Principal Component Analysis (PCA)), Deep Learning, NLP, Data Mining, Scikit-learn, Keras, TensorFlow, Git
  • Programming Languages: Python, R, Java
  • Data Visualizations: Tableau, Matplotlib, Plotly, Rshiny, Dash
  • Presentation: Communication, Dashboards

Certificates

coursera-568rv34hb5xp-page-001   coursera-hawnthrksytn-page-001 coursera-kkhjesbducxf-page-001 dataminingwithweka-1moredataminingwithweka_2 advanceddataminingwithweka-3

1 thought on “About Me

  1. Hi,

    I would like to know about the ML deployment method or process in production. And also the evaluation techniques like A/B testing or online evaluation.

    Also is there is a possibility to get associated with you for internship in any kind of projects.

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