Siddhant Kadam

A Data science enthusiast with over 2 years of experience as a Data engineer worked with high quantity of data and gave quality results throughout projects allocated . I have a confident approach towards analysing problems or issues within development and implementing changes required . Have majorly worked on Python, azure, snowflake and aws.

Hardworking employee with customer service, multitasking and time management abilities. Devoted to giving every customer a positive and memorable experience. And even awarded as backbone builder.

REFERENCES Prajakta Chalukya from Dataeaze systems Prajakta.chalukya@dataeaze.io +91 7720-815586 Ajit Ratnaparkhi from Dataeaze systems Ajitr@dataeaze.io +91 93254 24867

• Build data pipeline to move data from sources to SnowFlake
  Role : Data Engineer
  About project : For a Transit Agency data platform, it is required to build ADF pipelines,
• To move data from mail as source to Snowflake.
• To upload static data files to snowflake
• Contribution : Built ADF automation to pull data from Mail and static file and upload to snowflake. Built separate data copy pipelines for this.
  Tech toolset : Azure Data Factory, Azure Delta Lake v2, Snowflake
  
  Build data pipeline to move data from SQLServer to Snowflake in Finance Project
  Role : Data Engineer, Bi Developer
  About project : A micro finance corporation where their backend is implemented with SQLServer, Salesforce and Hubspot.
  Contributed in movement of data from SQLServer to snowflake. Built ADF pipelines for this, setup alerts for these pipelines.
  Also created interactive tableau reports for financial departments. Built snowflake queries for reports and integrated with tableau.
  Tech toolset : ADF, Snowflake, Tableau
  
  Build data pipeline to move data from SQLServer to SQLServer
  Role : Data Engineer
  About project : Implement a POC on transformations of SQLServer Tables and store them
  Contributed in Transformation of data from SQLServer to SQLServer. Built ADF pipelines for this, setup alerts for these pipelines.
  Tech toolset : ADF
  
  DataOps Platform For Mlops operations
  Role : Data Engineer (Lead)
  About project : Create a DataOps platform for a Mlops platform to version ASR and NLP data and manage to create datasets.
  - Pioneered the development of a multifaceted DataOps platform that seamlessly integrates both CLI and GUI interfaces, catering to diverse user preferences and operational needs.
  - Collaborated closely with cross-functional teams to identify pain points in existing MLOps workflows and engineered a solution that addressed these challenges comprehensively.
  - Designed and implemented a user-friendly CLI that empowered technical users to interact with the platform programmatically, enabling streamlined automation and rapid execution of tasks.
  - Crafted an intuitive GUI that facilitated non-technical users in model tracking, labelling and monitoring, and management, fostering cross-team collaboration and ease of use.
  - Incorporated version control and model tracking features into both interfaces, ensuring the traceability and reproducibility of models throughout their lifecycle.
  - Authored detailed documentation and provided training sessions to ensure a smooth transition to the new DataOps platform, facilitating team adoption and proficiency.
  
  Technologies Leveraged:**
  - Programming: Python
  - CLI Framework: Typer
  - GUI Framework: django , react.js
  - Data Processing: Pandas, NumPy
  - DevOps Tools: Git, GitHub
  
  **Outcomes:**
  The hybrid DataOps platform I conceived and developed had a profound impact on our MLOps operations, catering to both technical and non-technical users. By offering versatile interfaces and streamlining tasks, the platform contributed to improved collaboration.

Hireeaze : Recruiting tool:

Role : Data Engineer (Lead)

Led a focused team in developing an AI-driven backend tool, leveraging Large Language Models (LLM) to transform hiring workflows. This tool skillfully interprets job requirements and candidate data, automating the match-making process between job openings and applicants.

Key Achievements:

• AI-Powered Screening: Introduced LLMs to autonomously screen candidates, significantly reducing manual effort and enhancing the accuracy of matching candidates to job specifications.
• Candidate Ranking Algorithm: Created an algorithm to efficiently rank candidates, streamlining the shortlisting process and focusing on the most promising applicants.
• Interactive Dashboard Design: Crafted wireframes and a real-time dashboard for hiring managers, providing a seamless and intuitive user experience and clear visibility into the hiring pipeline.
• Feedback Loop for AI Refinement: Implemented a feedback mechanism to continuously improve the AI's predictive capabilities, ensuring the tool learns from hiring outcomes and user insights.
• Customization and Scalability: Worked in tandem with HR to ensure the tool's adaptability to various departmental needs, resulting in a versatile solution applicable across the organization.
• Data Security: Ensured the tool adhered to strict data privacy protocols and legal compliance, protecting sensitive candidate and company information.
• Leadership and Team Growth: Led the team not just in technical development, but also in fostering an environment that values creativity and innovation, culminating in a product that elevates the hiring experience.
  

Project : Email Attachment Parsing Automation Pipeline with Gen AI for Insurance Brokers

Role : Data Scientist 

About Project : The Email Attachment Parsing Automation Pipeline project for insurance  brokers was designed to streamline the validation of email attachments.  This automation aimed to identify attachments containing valid data  pertinent to policy requests, thereby enhancing efficiency and accuracy  over the traditional manual processes.

Contribution : I was entrusted with the leadership of a five-member team. Our collective objective was to scout for and integrate innovative technologies that would bolster the project's objectives. A notable aspect of my contribution involved the development and deployment of AWS Lambda functions. These functions were pivotal in automating the processing of incoming emails, enabling the system to execute code in response to email events without the need for dedicated server management.

A crucial facet of my role was to ensure seamless communication between our team and the client. This entailed regular discussions to pinpoint any project impediments, assimilate client feedback, and adapt our approach accordingly. Such dynamic communication was instrumental in keeping the project aligned with client expectations and in swiftly navigating through potential challenges.

A significant portion of my responsibilities revolved around the integration of Generative AI (Gen AI) models into our pipeline. This involved a meticulous process of selecting the most apt Gen AI models that could effectively parse and interpret the diverse range of data encapsulated in various email attachments. The selection process was underpinned by several key steps:

Parallel to these technical endeavors, my team and I were engaged in an ongoing process of refining our testing methodologies. This continuous re-evaluation of test cases was aimed at bolstering the robustness and reliability of our solution, ensuring it could adeptly handle a broad spectrum of document types and data structures. This iterative enhancement process was pivotal in reducing manual intervention from insurance brokers, thereby significantly streamlining the policy request workflow.

In essence, my role as a Data Scientist in the Email Attachment Parsing Automation Pipeline project entailed leading a dedicated team in the strategic integration of cutting-edge technologies, fostering effective stakeholder communication, and spearheading the adoption of Gen AI models to automate and refine the processing of email attachments in the insurance sector.