United States

AI/ML Engineering

Master's degree

Business Analytics

Disha

Leadership Coach + Sr. Research Scientist

Communication

Collaboration

Problem-solving

Time management

Critical-thinking

Empathy

Conflict resolution

Stakeholder management

Leadership

AI/ML

Conversational AI

Negotiation

Jupyter Notebook

Python

NLTK

Pandas

PyTorch

TensorFlow

Amazon Lex

Cursor

Airflow

Keras

Numpy

Organisation skills

Customer service

Chatbot

Machine learning

Integration Testing

Introduce yourself, define mentoring expectations and goals. Learn and grow together.

General mentorship

Learn essential burnout prevention techniques and replenish physical and emotional energy.

Managing burnout

Talk to people who have learned to manage their imposter syndrome.

Dealing with imposter syndrome

Navigating micro-aggression, getting paid what you are worth and finding a community.

Being a woman in tech

Tips for transitioning into a new role or company.

Changing roles

How to think about design career paths and what progression looks like for designers?

Design career path

Get actionable feedback on your resume or portfolio.

Resume and portfolio review

Practice HR, technical and behavioural interviews with mentors.

Practice mock interviews

Talk to people who've broken into the tech industry and learn their stories.

Breaking into tech

Learn the best ways to answer technical interview questions with frameworks

Acing technical interview questions

How to set up the team for success by defining and measuring team's performance.

Choosing metrics and team goals

Ways to provide constructive feedback to team members.

Giving effective feedback

Tips on how to work and collaborate with engineers and developers.

Working with engineers

I help women in tech navigate impostor syndrome and secure their next promotion.

Optimizing customer contacts for Amazon Customer Support using Generative AI and LLMs. Currently, I'm working on projects related to Multimodal multilingual CX, Content moderation, and LLM Evaluation.

Buyer Fraud Team -

Recent Projects:
Optimize Auto BatchPass for Customer Experience:
Minimize SLA misses without being reckless for better CX

Downstream Customer Impact:
Quantify the impact of enforcements and Customer friction activities on good Customers spend using causal impact analysis.

Drivers causing increase in bad debt:
Identify underlying causes affecting increase in bad debt

Virtual Investigator Project: 
Machine to automate decisions taken by human investigators who evaluate orders for fraud.

As a Data Scientist in Fraud and Risk Analytics team, I am responsible for modeling complex problems, discovering insights, and identifying opportunities through the use of statistical, machine learning, algorithmic, mining and visualization techniques. 

Responsibilities:
> Use predictive analytics and data mining to solve complex problems and drive business decisions. 
> Employ the appropriate algorithms to discover patterns of risks, abuse, and help reduce bad debt
> Design experiments, test hypotheses, and build actionable models to optimize business processes
> Solve analytical problems, and effectively communicate methodologies and results 
> Draw relevant inferences and insights from data including, identification of trends and anomalies
> Work closely with internal stakeholders such as business teams, product managers, engineering teams, and partner teams and align them with respect to my focus area

> Worked on data science projects – Churn Modeling, Recommendation engine for In-app stores, predicting player lifetime value, player segmentation
> Developed a 3 years Strategy and Road map to build Big data architecture and expertise in Analytics with a detailed execution plan for the next 6 months.
> Currently working on project to detect fraud and pirated players. 
> Mentor and enable team members to automate the existing dashboards for daily kpi reports using Tableau instead of Excel.
> Interact with other gaming companies like Blizzard Entertainment, Riot Games, etc to understand how data science is being implemented and affected them.
> Work with vendors like Microsoft, Teradata, datametica, etc to finalize on data architecture.
> Mentor two teams from ISB CBA batch 5 for their capstone projects.

> Projects - Forecasting weekly revenues and predictive models for customer retention and renewals.
> Consult with stakeholders and collaborate with other analysts to identify business needs and translate business requirements into features, functionality, and processes
> Conduct thorough data validation, consolidation, formatting, cleansing, and maintenance
> Extract and interpret data, analyze results and present data in meaningful representations
> Create and manage reports, dashboards, and processes to monitor data quality and integrity
> Manage multiple projects with competing priorities

Worked on multiple projects, taking up different roles, working on data mining and analysis for Life Sciences domain.
- Worked as Data Expert on a ETL development project using SAS Enterprise Guide for data mining
- Worked extensively on Google Analytics / Digital Marketing, integrating offline and online advertising campaigns
- Played BA-QA role carrying out the whole STLC process, handling client-facing roles, being involved in all the phases of project.
- Acted as SME and Offshore Coordinator.

Disha🇺🇸