Kamna Sinha

Founder

Bengaluru, Karnataka, India13 yrs 6 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Founder of a successful B2B SaaS startup.
Expert in data analytics and machine learning pipelines.
Proven track record in product management and market strategy.

Stackforce AI infers this person is a SaaS and Data Analytics expert with strong product management skills.

Contact

kamna.sinha@gmail.com LinkedIn

Skills

Core Skills

Data AnalyticsProduct ManagementData ValidationMachine LearningBig DataData Quality

Other Skills

A/B TestingAPI DevelopmentAPI ManagementAmazon Web Services (AWS)Analytical SkillsApache AirflowAutomation Test FrameworkBig Data AnalyticsBusiness-to-Business (B2B)ClassificationCluster AnalysisClusteringCompetitive AnalysisCustomer ExperienceCustomer Research

About

With around 12 years experience in building and delivering world class Data products in big names in the software industry , and taking my own startup from conceptualization to profitable stage, I come with an in-depth understanding and working knowledge of the entire ecosystem around Data and AI . At Sensewithai, My expertise in Data analytics , ML pipelines and SaaS-based API platforms has been central to developing solutions that curate and analyze vast data sources, delivering actionable insights to our clients. My expert understanding of product and market fit, coupled with a strategic approach to growth and development, has been instrumental in driving Sensewithai's success. We have excelled in aligning our technological capabilities with market demands, thereby ensuring that our solutions resonate with the business challenges faced by our clients. Through close collaboration with data scientists and engineers, we've crafted a suite of data validation and analytics solutions that empower businesses to harness the full potential of their data. Find out more about my work @https://medium.com/@kamna.sinha

Experience

Sensewithai

Founder & CEO

Mar 2020 – Present · 6 yrs · Bangalore Urban, Karnataka, India

Sensewithai [Sense-With-AI] is a SaaS based API Platform to automatically crawl, extract and curate data from various data sources like web, files, internal corporate data [ structured/ unstructured] and use composable APIs from the platform to create consumable analytics , insights created by ML pipelines, LLMs, for various business use cases.
As a Founder to B2B SaaS startup, I played multiple roles few being :
1. Heading the business development : create a roadmap for growth, market analysis, competitor analysis, sales strategy , GTM for the Data Platform .
2. Team development : people management, hiring, conflict resolution.
3. Product development : setting direction for data platform development, feature prioritization, project management, market analysis for similar features from competitors, pricing for b2b offering, API development and customer feedback analysis.
4. Head engineering efforts : plan engineering efforts and resources, align with product roadmap, work on backlogs of customers , bugs and feature enhancements , take decision on the correct machine learning algorithms to be used, ensure best coding practices, ensure quality assurance of features/APIs,
data analytics [ Extensively coded in Python/Pyspark for Analytics] ,
Played key role in : Data Architecture and design, System Design, Cloud management, ETL pipeline , ETL Testing efforts.
5. Data Science and AI decisions : choose the right ML engineer and data scientists to work with for a given project, choose the most appropriate AI approach to solve customer problems, ensure model quality and testability, ensure Data Quality, Data Quality Validation, choose the right tools, keep track of data sources and volume of raw data to be utilized.

data quality validationQuantitative AnalyticsStrategic RoadmapsTechnical ArchitectureBusiness-to-Business (B2B)Software as a Service (SaaS)+47

Clustr

Senior SDET [ Data And ML ]

Apr 2019 – Mar 2020 · 11 mos · Greater Bengaluru Area

ClustrData envisioned building a Product Catalog by assimilating various data sources like internal Tally Data Sources and External Data sources like the Web to build a Product Knowledge Graph that would serve as a backbone for providing product catalog experience to Tally Customers. Building knowledge required various Machine Learning Techniques like Entity Disambiguation, Entity Mapping, Probabilistic Deduplication, and Clustering.
As a Senior SDET engineer, my role was to work closely with data science and engineering to design and architect various approaches to validate the semantic and syntactic correctness of the Product Knowledge Graph created. This required building various statistical data validation modules using network analysis techniques.
Skills Required: Network Analysis, Working closely with Product to understand Product Requirements keeping Data Quality Validation in mind

Network AnalysisData Quality ValidationMachine LearningStatistical Data ValidationData Validation

[24]7

Senior SDET

Mar 2014 – Feb 2016 · 1 yr 11 mos · Bangalore Urban, Karnataka, India

In 24/7 one of the subsystems responsible for building Uber Context of customer profiles is BDP(Big Data Platform). Uber Context is a unified profile view of customers across various communication channels. The responsibility of BDP was to generate this Uber Context by joining and cleaning data from multiple sources. The quality of this data was very important for the downstream subsystem to perform analytics like Churn Analytics etc.
As an engineer, my role was to work closely with data science and engineering teams to build big data components(spark jobs) for performing large-scale statistical data validation to ensure generated uber context data was of high quality and reliability.
Skills Required: Data Validation Fundamentals, Statistical Data Validation , Test Design, Automation Test Framework

Data Validation FundamentalsStatistical Data ValidationTest DesignAutomation Test FrameworkData ValidationBig Data

Dell emc

Software Quality Engineer

Aug 2009 – Mar 2014 · 4 yrs 7 mos · Bangalore

EMC Control Center was a massive data collection and analysis service bundled along with EMC Proprietary storage and network products like Symmetrix, Clarion Etc. The objective of the product was to be a single point of collection of all the telemetry data generated by the various EMC Products running inside a data center.
As an engineer, my role was to ensure data quality validation of the various telemetry data collected. This was done by designing statistical data quality and reliability validation test cases to ensure the proper health of the data collected by various components running across the data center where ECC was deployed.

Data Quality ValidationStatistical Data ValidationData Quality