Headline

Data Analyst | Power BI · SQL · Python | 4+ Yrs in Data QA, Insurance Analytics & AI-Assisted Pipelines | Open to Remote Roles

About

I turn raw, messy data into decisions that move the needle. With 4+ years across data QA, insurance analytics, actuarial support, and business intelligence — at YipitData and EXL Services — I've built and validated data pipelines at scale, delivered Power BI dashboards relied on by actuarial and underwriting teams daily, and applied AI/LLM-assisted workflows to accelerate data quality at speed. What I bring to the table: ▸ Data validation & QA — end-to-end pipeline validation using Python, SQL, Regex, Kibana, and AI/LLM-assisted classification tools ▸ Insurance & actuarial analytics — pricing rate indication, regulatory rate filing, premium impact and dislocation analysis using Radar WTW ▸ Power BI expertise — DAX measures, Power Query, star schema modelling, role-based security, and executive dashboards ▸ Data annotation & NLP — NER tagging, intent classification, OCR correction with documented 95%+ accuracy across projects ▸ PII compliance & data governance — detection, masking, and validation using Templatizer, AI Mask, and Full Mask toolsets I thrive in roles that demand both analytical rigour and cross-functional collaboration — translating complex data findings into clear business decisions for actuarial, underwriting, operations, and product teams. Currently open to remote or India-based roles in: → Data Analytics / Business Intelligence → Data QA / Data Operations → Insurance / Actuarial Analytics → Data Annotation / AI Training 📩 akanksha.meshram45@gmail.com | Pune, Maharashtra | Open to relocate for the right opportunity

Location

Pune District

Work experience

Data QA at YipitData, Feb 2025 - Present · 1 yr 5 mos, Bengaluru, Karnataka, India, Led end-to-end validation of email receipt ingestion and transaction data pipelines (Edison E-Receipt & SpendHound), processing 500K+ daily records with maintained data accuracy standards for downstream analytics teams. ▸ Reduced pipeline defect reopen rate by 30%+ through structured root cause analysis and post-fix rerun validation across ingestion failures, email drops, and vendor/product match confidence checks. ▸ Applied AI/LLM-assisted workflows — including Generative AI classification, Regex, Templatizer, AI Mask, and Full Mask — to accelerate PII detection, data classification, and compliance validation at scale. ▸ Built and monitored real-time data quality dashboards in Power BI and Kibana, surfacing anomalous trends and ingestion failures to engineering and analytics stakeholders within 24-hour SLA windows. ▸ Managed defect lifecycle in Jira — logging, triaging, and coordinating resolution with AI + human review teams — improving SaaS spend reporting accuracy and data integrity across client deliverables. ▸ Collaborated with cross-functional teams to reconcile SpendHound spend insights, reducing misclassified vendor records by 20%+ through iterative data correction workflows. Tools: Python · SQL · Power BI · Kibana · Jira · Templatizer · Regex · Generative AI · Excel

Business Analyst at EXL, Dec 2022 - Jan 2025 · 2 yrs 2 mos, Pune District, Maharashtra, India, Supported actuarial pricing, regulatory rate filing, and implementation for Residential, Recreational, and Personal Auto insurance product lines across multiple US carrier clients. ▸ Executed insurance pricing revisions using Radar WTW models — delivering rate adequacy improvements and loss ratio optimisation across 3 product lines, directly influencing underwriting strategy decisions. ▸ Prepared and validated large-scale datasets via Cognos and Excel; performed premium impact, dislocation, and distribution analysis comparing legacy vs revised rate structures for actuarial sign-off. ▸ Developed 8+ Power BI dashboards (DAX, Power Query, star schema, RLS) for actuarial, underwriting, and business leadership — enabling self-serve reporting and reducing ad hoc data requests by 40%. ▸ Applied Generative AI–assisted NLP classification to enhance claims description categorisation, fault attribution, and driver behavioural risk segmentation — improving model input quality for pricing decisions. ▸ Partnered with actuarial teams to produce regulatory rate filing submissions, ensuring compliance with carrier and state requirements across multiple jurisdictions. ▸ Presented data-driven pricing insights to senior stakeholders — translating complex actuarial outputs into actionable business recommendations for underwriting and product teams. Tools: Radar WTW · Power BI · Python · SQL · Excel · Cognos · DAX · Power Query · Generative AI

Data Annotation Engineer at Systems Plus Transformations, Feb 2021 - Feb 2022 · 1 yr 1 mo, Pune District, Maharashtra, India, Performed structured data annotation across NLP, NER, intent classification, and OCR correction tasks to train and validate machine learning models for enterprise AI applications. ▸ Maintained 95%+ annotation accuracy across NER tagging, intent classification, and OCR correction tasks — consistently exceeding quality benchmarks across project deliverables. ▸ Annotated and validated thousands of text records daily using structured guidelines, contributing to high-quality training datasets for production NLP models. ▸ Applied domain knowledge to resolve edge cases in entity recognition and intent labelling, ensuring consistency and reliability of annotated outputs for downstream model training. ▸ Collaborated with QA leads to review annotation quality, flag ambiguities, and iterate on labelling guidelines — improving inter-annotator agreement on complex classification tasks. Tools: NER · NLP · Intent Classification · OCR · Data Annotation · Excel · Quality Assurance

Education

Bachelor of Engineering - BE, Mechanical Engineering, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad

diploma in mechanical engineering, Mechanical Engineering Related Technologies/Technicians, Government Polytechnic college