Data Scientist · Analytics Engineer

I transform complex business data into clear, actionable insights.

I build data pipelines, risk signals, statistical models, and executive dashboards that help teams understand performance, detect anomalies, and move faster with evidence.

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Samprithi Ravisanker smiling in a yellow flower field with mountains in the background

Currently

Digital Data Scientist at CHEP, focused on operational analytics, investigative data science, and business-facing metrics.

I like working on

Problems where data quality, stakeholder trust, and measurable impact matter as much as the model.

Selected Impact

From raw data to usable signals.

My work sits at the intersection of analytics, automation, and machine learning: building reliable workflows and translating findings into action.

15+

Executive metrics

Designed and maintained risk and performance metrics used by stakeholders to monitor operational issues and guide intervention.

80%

Less manual collection

Automated web scraping and data collection workflows that reduced repetitive manual work and improved analytical turnaround.

Production improvement

Used mixed-effects regression to quantify plant-level operational drivers and benchmark performance variability.

Experience

Analytics work with measurable outcomes.

Feb 2025 — Present

Digital Data Scientist · CHEP

Built Databricks pipelines, statistical models, risk metrics, and dashboards for operational and investigative analytics.

Sept 2023 — Feb 2025

Digital Operations Analyst · CHEP

Automated data collection, executed SQL/Python ETL workflows, and developed Power BI dashboards for operational KPIs.

Apr 2020 — Jul 2021

Data Analyst · SS HVAC Engineers

Built ETL workflows and QlikView dashboards to support business performance reporting and cross-functional analysis.

Jun 2019 — Mar 2020

Data Analytics Intern · Arvind Internet

Worked with customer and product data, API testing, churn analysis, A/B testing, and ad hoc dashboards.

Skills

Tools I use to build data products.

Analytics

SQL, MySQL, MSSQL, Python, R, Pandas, NumPy, statistical analysis

Data & Cloud

Databricks, PySpark, AWS, GCP, ETL workflows, validation checks

BI & Dashboards

Power BI, Tableau, DAX, QlikView, KPI design, executive reporting

Machine Learning

PyTorch, TensorFlow, Keras, TFLite, clustering, embeddings, anomaly detection

Projects

Analytics, ML, and dashboarding work.

A single collection of projects across machine learning, business intelligence, forecasting, NLP, and operational analytics.

Machine Learning · NLP · Topic Drift

User Interest Evolution & Topic Drift Detection

Built a machine learning system to detect emerging, declining, and shifting topics over time using public text data.

Approach Embedding-based clustering, temporal drift analysis, and behavioral trend surfacing.

Tools Python, Hugging Face, SBERT, OpenAI embeddings, clustering.

Outcome Created a workflow for proactively identifying new trends and shifts in behavior.

LLM · Automation · Evaluation

LLM-Powered Email Categorization

Designed an offline-first email triage workflow using a locally deployed quantized LLM for low-latency classification.

Built Schema-constrained outputs with an SQLite-backed audit and evaluation workflow.

Goal Reduce misclassification risk and make email automation safer.

Operational Analytics · Risk Signals

Asset Leakage & Anomaly Detection

Applied unsupervised learning and statistical analysis to detect anomalous asset reuse patterns and leakage risk.

Built Customer and regional risk views for investigative analytics.

Impact Supported investigations that contributed to contract renegotiation impact.

Streaming Analytics

Comparative Analysis: Netflix, Hotstar & Hulu

Compared streaming platforms through an interactive dashboard, making platform differences easier to explore visually.

View project

How I Work

I care about the full analytics lifecycle.

Good data work is not just a chart or a model. It is the path from a messy question to a repeatable decision-making system.

Clarify the decision

I start by understanding what action the team needs to take and what signal would make the decision easier.

Build reliable data foundations

I use SQL, Python, PySpark, validation checks, and Databricks workflows to make analysis repeatable.

Model what matters

I apply statistical analysis, regression, clustering, and anomaly detection where they create real business value.

Make it usable

I turn results into dashboards, metrics, and simple narratives that stakeholders can actually use.

Certifications

Courses and credentials that support my analytics toolkit.

These certifications reinforce the tools I use for data preparation, analysis, dashboarding, and data warehousing.

Udemy

Microsoft Excel

Spreadsheet analysis, formulas, reporting, and business-ready data workflows.

Udemy

MySQL

Relational querying, joins, filtering, aggregations, and database analysis fundamentals.

Udemy

Tableau

Dashboard design and interactive visual analytics for communicating business insights.

Google

Google Analytics for Beginners

Web analytics basics, audience behavior, acquisition, and performance reporting.

Snowflake

Data Warehouse

Modern data warehousing concepts for scalable storage, querying, and analytics workflows.

Let’s connect

Looking for data science, analytics, or dashboarding roles.

I’m interested in teams where analytics is close to the business problem and where clean data work can create real operational impact.

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