Ian Pointer
Senior Manager (Data Science)
Cincinnati, Ohio, United States
Summary
👤
Senior🎓
Top SchoolIan Pointer is a Senior Manager specializing in data science and search, bringing 15 years of experience designing and operating large-scale ML and data platforms across cloud environments like AWS and GCP. He blends hands-on deep learning work (PyTorch models, spectrogram/audio nets, model quantization, Hugging Face) with strong systems and backend engineering—comfortable in languages from Python and Scala to Go and Rust—and longstanding expertise in streaming and batch ecosystems (Spark, Kafka, Flink, Beam). At Lucidworks he leads data science for search, after architect and senior data engineering roles where he built production pipelines, recommendation and NLP systems, and tuned high-throughput infrastructure. He’s an active open-source collaborator (contributions to spark-solr and practical PyTorch examples), which reflects a practical focus on making ML reproducible and production-ready. Known for operating at the intersection of ML research and reliable distributed systems, he often surfaces pragmatic optimizations that shrink model latency or operational cost.
15 years of coding experience
14 years of employment as a software developer
BSc(Hons) First Class Computer Science, BSc(Hons) First Class Computer Science at University of ManchesterUniversity of North Carolina at Chapel Hill
Github Skills (15)
mask-rcnn10
javas10
pytorch10
machine-learning10
faster-rcnn10
spark10
trainings10
solr10
modeling10
java10
model-optimization10
transformers9
computer-vision8
sql7
scala7
Programming languages (5)
Github contributions (5)
falloutdurham/beginners-pytorch-deep-learning
Aug 2019 - Sep 2021
Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning
Role in this project:
ML Engineer
ML EngineerContributions:61 commits, 35 PRs, 58 pushes in 2 years 1 month
Contributions summary:Ian primarily contributed to the development of deep learning models using PyTorch. They implemented core model architectures, including an AudioNet and a CNN, and experimented with finding optimal learning rates and fine-tuning models. Furthermore, the user worked on image processing, including the use of spectrograms and class activation mappings, and explored model quantization techniques to optimize model performance. The user also made changes that involved the use of the Hugging Face Transformers library.
pytorchpythondeep-learningmachine-learningnotebooks
lucidworks/spark-solr
May 2020 - Dec 2021
Tools for reading data from Solr as a Spark RDD and indexing objects from Spark into Solr using SolrJ.
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 40 commits, 35 PRs in 1 year 7 months
Contributions summary:Ian contributed to the development of the `spark-solr` project by merging various pull requests. The commits primarily involve merging changes related to bug fixes, feature enhancements, and general code improvements. These changes touch upon core functionalities, including interaction with Solr Cloud clusters, SQL testing, and adjustments to the example query and ML pipeline, suggesting a focus on improving the project's capabilities for reading data from Solr and indexing objects.
solrindexingsparksolrjrdd
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