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Berkshire Hathaway

Marsh McLennan (NYSE: MMC) is the world’s leading professional services firm in the areas of risk, strategy and people. The Company’s more than 85,000 colleagues advise clients in 130 countries.  With annual revenue of over $20 billion, Marsh McLennan helps clients navigate an increasingly dynamic and complex environment through four market-leading businesses. Marsh provides data driven risk advisory services and insurance solutions to commercial and consumer clients. Guy Carpenter develops advanced risk, reinsurance and capital strategies that help clients grow profitably and pursue emerging opportunities. Mercer delivers advice and technology-driven solutions that help organizations redefine the world of work, reshape retirement and investment outcomes, and unlock health and wellbeing for a changing workforce. Oliver Wyman serves as a critical strategic, economic and brand advisor to private sector and governmental clients. 

Follow us on Twitter at @marshmclennan.

M&A and corporate advisory firm focused on energy, healthcare, and multiple sectors

To deliver a private AI-powered Solution to assist with code analysis, understanding architecture, and generating user stories. The proposal suggests running a Proof of Concept (PoC) for one CTC MF Application to validate the efficiency of the proposed AI-powered knowledge base in analyzing PL/1 code bases. The results of the PoC will be compared with the manual analysis captured by the App team. POC helps to figure out whether Gen AI can efficiently analyze data sources such as video recordings, images, HTML pages.

Technology used: Python, SQLAlchemy, Flask, Azure OPENAI API key, OpenAI Embedding, Deep lake Vector store, Redis search, Azure Blob storage, psycopg2.

VTB Capital Investment Management

Da Vinci Capital

Euroventures

Renaissance Investment Management

RTP Global

Russia Partners

Merifin Capital

Edventure Holdings

Riverview Trust Company

EPAM provides software development services to clients across Finance, Travel &amp; Hospitality, Software, Retail and other industries. Provides product development services, Internet of things development, enterprise applications, data analytics solution, collaboration suites and more. Customized solutions for digitization of business, Went Public on Feb 8, 2012. Carries out projects for large customers in more than 30 countries around the world. Customer include Renaissance capital, Barclays capital,Sber bank, Coca-cola, Kingfisher, Bosch, Volkswagen, Hyundai, T-systems, British Telecom etc. In 2020, the company recorded annual revenues of $2.7 B and a net profit of $327.2 M.

Provider of software development, data analytics, and other IT suite services

I have worked on PySpark, Databricks Notebook, ADLS, MS-SQL and many other technologies where 
I developed a solution for data processing (fetching data from silver and gold layer on ADLS) as per business need.  Right from the tuning of Databricks Cluster to optimizing the code for faster processing.
Working On Delta Table, learnt a lot about its similarity with Relational Database.
There were many source of Data and one of them was ERS from where it was copied on Cloud as Raw. 
There was Azure Data Factory pipeline which transform the data from raw layer into Bronze layer.

Developed a Rest API using Flask Framework for Data consumption from MS-SQL database. Created Multiple Python      
Script solution for data Processing. Understood the Business Part and Performed troubleshooting by changing the existing  Code and adding new functionality written in Django Framework. I was Fully responsible for the deliverables.  I was also involved Month Cycle activities with Data Engineering Team where our Task was to Run SSIS Packages written in SQL to consume the data file Upload by 143 Territories and Inserting them into Corresponding Tables in Different databases by transforming them using ETL tool such SSIS.

We had consumed the data from s3 which was coming from multiple sources. Created ETL Pipeline using AWS Glue and after processing needs to load in multiple target like RDS, Redshift.   
Docker Compose file creation for the webservices deployment and managing them, working on AWS services.  Technology used: Python, AWS  Services, Lambda, Glue, Redshift, DynamoDB, EMR, Kafka, Docker, Kubernetes.

PwC Acceleration Centers (ACs), formerly Service Delivery Centers, are diverse, global talent hubs focused on delivering value to our clients – working with global client engagement teams across the PwC network. Driving new digital ways of working, ACs help unlock new opportunities for our people, organization and clients through competency enhancement, collaboration, automation, quality, innovation and integrated global delivery.

Our Acceleration Centers span 11 cities and seven countries, and provide virtual client engagement support around the world and across all of PwC’s services and capabilities.

Professionals in our innovative Acceleration Centers (ACs) are part of inclusive teams who work together across functions and geographies; engage in challenging projects; and provide distinctive services to support client engagements through enhanced quality and innovation. A role in our Acceleration Centers will provide growth potential and valuable learning opportunities.

A community of solvers combining human ingenuity, experience, technology and products to help organizations build trust and deliver sustained outcomes.

Project#1: – MFGADM ||Python Backend Engineer    
Development of web applications using Flask Framework and other Backend tools and libraries. Consumption of Data and other resources was the main the main goal for our client and we had developed multiple endpoints for the same.    
Tools and Technology used: Python, Flask, SQL Alchemy, Alembic, Jupyter Notebook, SQL, pyodbc.    
    
 Project#2: – MFGADM || Client: Cisco MFGADM || Ticket Sentiments Analysis || CoE     Data Engineer and ML.    
Our Project was a part of the Journey Automation for Identifying Potential Customers and actual customers for a particular Hardware and Software products and sending them the marketing emails. The content of each emails depends on the product and common customer details. For all these works we were sourcing the data for each product campaign, Creating and Running Python Scripts for different Campaigns, filtering out Customer data based on Business Rules. QA Automation. Once the data is sourced and processed in Hadoop, the data is activated and deployed on a marketing tool names Eloqua through which Scheduled Marketing emails are sent to the customers.        
Worked on Multiple Projects where I developed Machine Learning Models based Data wrangling, processing, analysis anddata visualization. Creating Predictive Model using Machine Learning Algorithm for e.g:- Linear regression,    
Logistic,Decision Tree, KMeans, KNN classifier, PCA etc. Hyper-parameter Tuning Using Grid Search and Random Search,    Confusion matrix, Cross Validation    
        
Tools and Technology used: Python ML libraries,Pandas, Numpy, Scikit-learn, Nltk etc, Jupyter Notebook, SQL,  Spark Hadoop, Airflow, kafka.

ChrysCapital

Nepean Capital

Infosys Technologies was started in 1981 by seven people with US$ 250. Infosys’ service offerings span business and technology consulting, agile consulting, application services, systems integration, product engineering, custom software development, maintenance, re-engineering, independent testing and validation services, IT infrastructure services, and business process outsourcing.  Infosys has a global footprint with over 50 offices and development centers in India, China, Australia, the Czech Republic, Poland, the UK, Canada, and Japan. Infosys has over 100,000 employees.  Infosys takes pride in building strategic long-term client relationships. Over 97% of its revenues come from existing customers. In 2020, the company recorded annual revenues of $13.5 B and a net profit of $2.6 B.

Provider of IT suite solutions and AI consulting services