gjreda

The easternmost portion of Northwestern's campus also has a nice walking/biking path along the lakeshore with a great view looking back towards the Chicago skyline.

    Location: Chicago, IL
    Remote: Yes
    Willing to relocate: No
    Technologies: python, machine learning (ML), AI, LLMs, HuggingFace, scikit-learn, pydata stack (pandas, numpy, scipy, matplotlib, etc), FastAPI, Pydantic, SQL, Airflow, Spark, Celery, AWS
    Résumé/CV: https://docs.google.com/document/d/1hJpdKFyDNzT4Hs2Ml7XjIBzmSDFSZTT_cFNYp4sPB3w/edit?usp=sharing
    Email: [email protected]
    Website: https://gregreda.com/

I've had a blast working on the earliest data science problems at Grubhub, to building and leading the data science org at Sprout Social, to scaling real-time logistics systems during the rapid, covid-fueled growth days of Instacart.

After taking a health-related career break [1], I'm ready to get back to work. I’m looking for an individual contributor role in engineering and AI/ML and am particularly excited about roles that have a platform or infrastructure component, allowing me to build systems and tools for researchers, engineers, and scientists.

1. https://gregreda.com/2022/11/30/this-ones-for-me/

I've mostly written technical, code-centric posts on Python, ML, and data science. Some of my early posts (2013) were wildly popular at the time and hit the top of HN and various subreddits.

I haven't written much recently, but I've been trying to branch outside of technical posts as I felt like my profession had started to become too much of my identity.

The post I'm most proud of:

- https://gregreda.com/2022/11/30/this-ones-for-me/ - Feeling pride and catharsis after years of bad health luck (leukemia, bad bike crash, cardiac arrest).

My most popular posts:

- https://gregreda.com/2013/03/03/web-scraping-101-with-python... - Web scraping tutorial using Python and beautifulsoup

- http://www.gregreda.com/2015/02/15/web-scraping-finding-the-... - Another web scraping tutorial with Python, but this time for sites that dynamically load content

- https://gregreda.com/2013/10/26/intro-to-pandas-data-structu... - The start of a series of posts on Python's pandas library

- https://gregreda.com/2013/07/15/unix-commands-for-data-scien... - Some useful unix commands for data processing

- https://gregreda.com/2015/08/23/cohort-analysis-with-python/ - Tutorial on doing cohort analysis using Python and pandas

- https://gregreda.com/2017/01/07/freelance-data-science-exper... - My experience as a freelance data scientist

- https://gregreda.com/2018/02/04/hiring-data-scientists/ - My approach to hiring data scientists (though my thoughts on this have evolved over the last five years).

Some context: I've been a jack-of-all-trades data scientist / machine learning engineer for the past 15 years (officially titled as an MLE the last four years).

I share that only because I think it plays a role in how I'm typically accustomed to working.

1. I found LangChain to be overkill for this use-case. While it might allow some to move more quickly when building, I found it to be cumbersome. My suspicion is this is largely because of my background - I understand how to build much of what's "under the hood" in LangChain. Because of this, I think it felt overly abstracted and I found the docs difficult to navigate and sometimes incomplete.

2. I used Qdrant via their docker image and it was simple to setup and start using. I didn't try to push the limits with it, so I can't say anything about performance. Because Qdrant runs as an http service, I found that it didn't fit well into my workflow - I'm accustomed to being able to visually inspect my data inside the interpreter, debugging, trying out commands, interacting and experimenting with my results, etc. Again, my suspicion is this is my own bias in how I typically work. Qdrant otherwise seemed very nice.

3. LanceDB felt powerful yet lightweight, and fit well into my workflow. It was far more intuitive for me. It was as if sqlite, the python data ecosystem, and a vector database had a child and named it LanceDB. Under the hood, it's built on Apache Arrow and integrates nicely with pandas, allowing me to seamlessly go from LanceDB table on disk, to pandas dataframe, and into some analysis or investigation of my LanceDB query results. This line [1] is a great example of why I liked it. This feels nicer to me than the world of API params and HTTP requests.

1. https://github.com/gjreda/scratch-pdf-bot/blob/main/gpt_pdf_...

I spent some time today exploring HuggingFace's Inference API but if the model is sufficiently large (> 10gb), HF requires you to use their commercial offerings.

I'm working on using it in a large project now.

[1] - https://github.com/gjreda/scratch-pdf-bot

Can you provide a source for this?

- https://www.cmegroup.com/trading/interest-rates/countdown-to...

I’d highly recommend the book The Philadelphia Chromosome if you’re interested in learning more.

> For restaurant inventory, we will rapidly expand our recent pilots of putting non-partnered restaurants on the platform. For reasons we’ve discussed many times, we believe non-partnered options are the wrong long-term answer for diners, restaurants and shareholders. It is expensive for everyone, a suboptimal diner experience and rife with operational challenges. With that said, it is extremely efficient and cheap to add non-partnered inventory to our platform and it can at least ensure that all of our current and potential new diners have the option to order from any of their favorite restaurants now, even if it’s not the best solution. By leveraging non-partnered options, we believe we can more than double the number of restaurants on our platform by the end of 2020.

> At the same time, because we know that partnered relationships are critical to the long-term success of this business, we will be investing aggressively in our independent restaurant sales organization to support converting as many of these non-partnered restaurants to partnered relationships as quickly as possible and to take advantage of other innovations in the restaurant space, like virtual restaurants.

https://s2.q4cdn.com/772508021/files/doc_financials/2019/q3/...

[1] https://youtu.be/a2oTps7tKS4?t=893

https://i.pinimg.com/originals/13/76/e2/1376e2e5690719701689...

As a rider, there's no "cost" to having both apps, so I'll just check both.

Similarly, as a driver, I'll just sit with both apps open and see which one I get a rider on.

I know that feeling well. Best of luck to you.

Sprout Social builds social media management tools for businesses of all sizes. We are built on the idea that the world is better when businesses and customers communicate freely. We exist to help streamline and enhance those conversations — with customers, prospects and enthusiasts.

Some openings:

    * Front-End Engineer (Seattle, WA)
    * Senior Front-End Engineer (Seattle, WA)
    * Senior Software Engineer - Platform (Chicago, IL)
    * Staff Software Engineer - Platform (Chicago, IL)

Our platform team uses Java, Python, MySQL, and NSQ, while our front-end team uses React, Redux, Ember, ImmutableJS, and Gulp, all to build highly scalable software that is used by more than 20,000 organizations around the world. Companies like Dropbox, Zendesk, Fender, Brooks Running, Seattle Cancer Care Alliance, and Evernote rely on our products to create stronger relationships with their customers.

If you're a creative, highly motivated, and inquisitive learner, we'd love for you to come build great software with us.

https://sproutsocial.com/careers/

Modern pandas is a bit more idiomatic now though: https://tomaugspurger.github.io/modern-1.html

Our team uses Python and its data stack (pandas, numpy, scikit-learn) along with a little Java to drive decisions and power software that is used by more than 17,000 brands around the world. Companies like Microsoft, Zipcar, Hyatt, Google, and Zendesk rely on Sprout to create stronger relationships with their customers through social media.

We’re looking for curious, analytical, and creative people to help utilize the vast amount of data we have. If you love finding ways of using data to build better products and solve problems, we’d love to talk with you.

Responsibilities:

    - Build predictive models in support of product and business goals
    - Conduct research to surface new opportunities and understanding
    - Build tooling for all of the above
    - Mentoring, teaching, and helping to level up the team

    - You have professional experience working on data-driven problems. Examples might be classification, recommendations, churn prediction, topic modeling, fraud detection, etc.
    - You’ve built production machine learning pipelines in Python or Java.
    - You have a strong understanding of statistics (probability, experimental design, bayesian methods).
    - You enjoy thinking about business and product, and how data can be used to inform or improve both.
    - You understand, appreciate, and can explain the tradeoffs between simple methods and complex models.
    - You probably studied a technical, quantitative, or analytical field throughout school, but also might have taken a different route. We’re looking for at least a Bachelor’s degree or equivalent.

    - Experience working with text data and using natural language processing methods
    - Experience with Apache Airflow, Spark, or AWS Data Products (EMR, Redshift, Kinesis)
    - Experience with deep learning toolkits, such as Keras, Theano, or TensorFlow

Sprout Social builds social media software that is used by more than 17,000 brands around the world. Companies like Adobe, Pivotal Labs, Google, Marvel, and Evernote rely on Sprout to create stronger relationships with their customers through social media.

    About the role
    --------------
    - The role is cross-functional and will work extensively with our Data Science, Marketing, and Optimization teams.
    - Our Data Science team uses Python and its data stack (pandas, numpy, scikit-learn, airflow, etc.), but we like R users too.

    Responsibilities
    ----------------
    - Use rigorous analysis, statistics, and a good deal of consideration to help us think about our funnel.
    - Plan, analyze, and report out on split testing experiments.
    - Use data to inform and influence product roadmaps and departmental goals.
    - Build tools to help us make better decisions and automate analyses.
    - Conduct research to surface new opportunities and understanding.

It'd be cool if they added a way to search through your list of reactions. This would allow you to effectively comment on an issue in an OS project, while simultaneously bookmarking it, so that you can go back and commit a fix when you have a free moment.

The paper's examples might be slightly dated as they use dplyr's predecessor, plyr. I imagine they're pretty similar though.

http://vita.had.co.nz/papers/plyr.html

I think in major cities, both Uber and Lyft are becoming commodities - both of them have ample enough supply that I just choose the cheaper option.

I don't actually believe that, but I do find myself thinking of other things I've needed/wanted when browsing Amazon.