I am Anmol Tripathi, a Quality Data Scientist and Machine Learning Engineer with 7+ years of experience across network operations, analytics, predictive modeling, deep learning, NLP, retrieval-augmented generation, and applied AI.
At Hach, I develop machine-learning and AI solutions for product-quality intelligence, including source-grounded RAG workflows, multi-stage NLP classification, analytics automation, and executive reporting. My public portfolio translates that experience into non-confidential, reproducible projects spanning statistical learning, neural networks, sequence models, Transformers, multimodal AI, retrieval, and deployment.
Current focus: reliable NLP and RAG systems, hybrid retrieval and reranking, calibrated prediction, transparent evaluation, and production-oriented ML workflows.
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10 end-to-end projects covering summarization, translation, retrieval, reranking, long-document QA, instruction tuning, VQA, Vision Transformers, CLIP, and RAG, with selected public demos.
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Applied generation systems including schema-aware Text-to-SQL, streaming-style speech recognition, and source-grounded data-to-text executive reporting.
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13 projects demonstrating statistical reasoning, EDA, feature engineering, forecasting, segmentation, model comparison, explainability, and business communication.
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Computer-vision projects organized around convolutional architectures, transfer learning, reproducible data pipelines, model evaluation, and visual error analysis.
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My dedicated portfolio website is the next stage of this professional portfolio and is currently being prepared. Once published, the Portfolio button above will link directly to the live website, bringing together my experience, flagship projects, deployed applications, technical case studies, and résumé in one place.
| Project | What it demonstrates | Explore |
|---|---|---|
| Schema-Aware Text-to-SQL | Fine-tunes CodeT5+ 770M with LoRA to generate SQLite from natural-language questions and database schemas, then validates and safely executes approved read-only queries. | Source code · Live application |
| AI Portfolio RAG Assistant | Uses MiniLM embeddings and hybrid retrieval to answer questions from public portfolio evidence with citations, relevance evidence, and latency details. | Source code · Live application |
| Vision Transformer Browser Classifier | Compares DeiT-tiny with ResNet-18, validates PyTorch-to-ONNX parity, visualizes attention rollout, and performs private WebGPU/WASM inference in the browser. | Source code · Live application |
| Streaming Speech Recognition with Whisper | Transcribes microphone or uploaded audio through a Whisper encoder-decoder workflow with chunking, timestamps, language detection, and robustness-oriented evaluation. | Source code · Live application |
| Data-to-Text Executive Report Generator | Converts structured KPI tables into source-grounded executive narratives while checking numerical claims, exposing source-cell evidence, and blocking unsupported statements. | Source code · Live application |
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Natural language → validated read-only SQLite
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Grounded portfolio answers with retrieval evidence
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Private in-browser inference with attention rollout
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Chunked transcription with timestamps and language detection
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| Capability | Evidence in this portfolio |
|---|---|
| Applied Data Science | EDA, statistical analysis, feature engineering, forecasting, predictive modeling, and business visualization |
| Classical Machine Learning | Classification, regression, clustering, ensemble modeling, calibration, benchmarking, and error analysis |
| Deep Learning | ANN, CNN, RNN, LSTM, bidirectional LSTM, autoencoder, encoder-decoder, and Transformer architectures |
| NLP & Retrieval | Text classification, summarization, translation, QA, semantic search, hybrid retrieval, cross-encoder reranking, and RAG |
| Computer Vision & Multimodal AI | CNNs, Vision Transformers, visual question answering, and CLIP image-text retrieval |
| ML Engineering | Reproducible training, modular inference, GPU/BF16 workflows, ONNX conversion, automated tests, CI/CD, and deployment |
| Analytics Engineering | Python and SQL automation, data validation, Power BI, Tableau, Excel, KPI reporting, and decision support |
Languages & data
Machine learning & applied AI
Engineering & communication
September 2024 - Present · United States
- Develop AI and machine-learning solutions for product-quality intelligence, analytics automation, and operational decision support.
- Designed an internal RAG and LLM-powered agent over structured quality records dating back to 2015 and thousands of technical documents, with source-grounded responses for approximately 50 potential users across R&D and quality teams.
- Developed a calibrated, multi-stage NLP framework for predicting interconnected quality categories using Transformer representations, sparse word- and character-level NLP, structured LightGBM models, ensemble learning, and chronological validation.
- Automate monthly, biweekly, and rolling-period quality analytics with Python, SQL Server, Power BI, and Excel, supporting KPI monitoring, root-cause investigation, data validation, and executive reporting.
Public repositories contain non-confidential portfolio work only. Company data, source code, internal systems, and proprietary methodology are intentionally excluded.
May 2023 - August 2024 · Tucson, Arizona
- Developed predictive workflows from longitudinal wearable-sensor data for approximately 135 research participants.
- Built SQL-to-model pipelines covering preprocessing, temporal feature engineering, sequence generation, model tuning, validation, and prediction-error analysis.
- Compared SVM, LSTM, bidirectional LSTM, CNN, autoencoder, ARIMA, and SARIMA approaches; LSTM-based modeling produced the strongest internal research result and refined the estimated labor-prediction window from approximately 14 days to approximately one day.
- Communicated findings and limitations to interdisciplinary collaborators without presenting research estimates as clinically validated predictions.
Earlier experience - analytics, retail operations, and network engineering
- Student Assistant Manager · University of Arizona BookStores (Dec 2022 - Aug 2023): analyzed approximately 80,000-100,000 monthly sales records, supported inventory planning, built Tableau reporting, and trained 8-10 team members.
- Student Assistant · University of Arizona BookStores (Sep 2022 - Dec 2022): analyzed sales and customer data, validated recurring reports, and supported operational decision-making before promotion within four months.
- Network Operations Center Engineer · Orange Business Services (Apr 2022 - Aug 2022): built an internally evaluated network-failure prediction proof of concept, automated operational analysis, and enhanced KPI dashboards.
- Associate NOC Engineer · Orange Business Services (Dec 2019 - Mar 2022): analyzed more than 10,000 network-performance metrics daily using Python, SQL, and Excel while supporting fault investigation and incident management.
- Graduate Engineering Trainee · Orange Business Services (Jun 2019 - Dec 2019): developed foundations in enterprise networking, monitoring, troubleshooting, data analysis, and operational reporting.
- Frame the decision - define the real problem, user, target, constraints, and meaningful success criteria.
- Establish evidence - validate the data, build baselines, compare candidates, calibrate where needed, and inspect failure modes.
- Engineer for reuse - separate training and inference, preserve metadata, test artifacts, automate validation, and document assumptions.
- Deliver responsibly - ground outputs in evidence, expose limitations, protect sensitive data, and communicate results for technical and business audiences.
I am extending my retrieval work through a new public, non-confidential RAG project focused on reliability and operational knowledge. The project is in active development and is intended to demonstrate:
- document ingestion, cleaning, chunking, and metadata-aware indexing;
- dense and lexical retrieval with reranking and source attribution;
- grounded question answering with abstention and citation checks;
- retrieval and generation evaluation, latency tracking, and failure analysis;
- repeatable local execution, testing, containerization, and deployment documentation.
The repository link and verified evaluation results will be added after the first complete release. No internal Hach data, documents, or proprietary implementation will be included.
| Institution | Program | Academic result |
|---|---|---|
| University of Arizona | Master of Science in Data Science | GPA: 3.889 / 4.000 |
| Texas McCombs School of Business | Post Graduate Program in Data Science and Business Analytics | Overall grade: 4.00 / 4.00 |
| Amity University | Bachelor's Degree in Electronics and Telecommunications | 2015-2019 |
Graduate curriculum
University of Arizona: Ethical Issues in Information; Introduction to Machine Learning; Data Mining and Discovery; Information Research Methods; Data Analysis and Visualization; Artificial Intelligence; Applied NLP; Neural Networks; SQL/NoSQL Databases; Independent Study.
Texas McCombs: Python for Data Science; Statistical Methods for Decision Making; Advanced Statistics; Data Mining; Predictive Modeling; Machine Learning; Time Series Forecasting; Tableau; SQL; Marketing and Retail Analytics; Finance and Risk Analytics; Capstone Project.
- Google Data Analytics Professional Certificate - Google
- Complete A.I. & Machine Learning, Data Science Bootcamp
- Microsoft Excel: Advanced Excel Formulas & Functions
- The Complete SQL Bootcamp: Go from Zero to Hero
- Introduction to Python
View credential records on LinkedIn →
| Path | Best starting point |
|---|---|
| RAG / Retrieval Systems | AI Portfolio RAG Assistant now; ReliabilityOps RAG after its verified first release |
| Transformers / Multimodal AI | Transformer projects - fine-tuning, retrieval, reranking, ONNX, multimodal AI, evaluation, and deployment |
| NLP / Generative AI | Encoder-decoder projects - Text-to-SQL, speech recognition, and data-to-text systems |
| Data Science / Analytics | Applied DS & ML portfolio - 13 projects spanning analysis, modeling, evaluation, and business communication |
| Computer Vision | CNN projects and Vision Transformer work |
| Sequence Modeling | Simple RNN → LSTM → Bidirectional LSTM |
| Neural Network Foundations | ANN projects - classification, regression, risk, optimization, embeddings, and deployment |
Open to Data Scientist, Machine Learning Engineer, NLP, and Applied AI opportunities.
Primary email · Alternate email
Preview résumé · Download résumé PDF
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