Hi, I’m Kayla Sox. I live in Los Angeles, and I’ve worked as a data person here for a while. I’ve had scrappy startup gigs. I’ve done contract work at big studios. I’ve also hopped on a game team in West LA. So this isn’t theory. It’s my day-to-day. And you know what? LA data jobs feel like a mash-up show—media, health, ads, games, and a bit of space rockets. It’s loud. It’s fun. It can be messy. But it pays.
If you want an even deeper dive, I originally unpacked the whole scene in a longer post called My Real Take on Data Science Jobs in Los Angeles—feel free to skim that too.
Let me explain what I mean, with real stuff I did and saw.
How I looked, and what actually worked
I used LinkedIn and Built In LA a lot. Wellfound helped with early stage stuff. Recruiters pinged me after I turned on “Open to Work,” but the best leads came from people. I met folks at Data Science LA, PyData LA, and a UCLA meetup by the Hammer. I brought a small project on LA scooter trips. That silly plot of rides by hour? It kicked off two interviews. People in LA love local data. It’s like a secret handshake.
To see how data (and radio waves) literally travel across SoCal in real time, I sometimes pull up vhfdx.net—it’s a quirky little site that maps live VHF propagation and makes you appreciate the local signal landscape.
Example 1: Health-tech in Culver City (my first LA contract)
I landed a 6-month contract at a small health-tech shop in Culver City. Think patient intake and claims. Four people on the data team. The stack was simple: Python, SQL, BigQuery, and Looker.
What I did:
- Cleaned claims data
- Found fraud spikes
- Built a churn model with XGBoost
- Shipped a Looker dashboard for ops
Pay was hourly. It came out close to a $150k base if full time. Not wild, but steady. Commute from Mid-City took 20 minutes on a good day. Parking was fine. Culture was “get stuff done.” Less meetings. More coffee. One funny thing: they said “data scientist,” but half my time was data engineering—pipelines, dbt, and fixing dates that broke. It was good for my skills, but it wasn’t pure modeling.
Example 2: Disney streaming team in Glendale (A/B testing life)
Next, I did a contract with a Disney streaming group in Glendale. Yes, that Disney. The work felt big. I focused on the signup funnel and ad targeting. Tools were BigQuery, Airflow, Jupyter, scikit-learn, and a homegrown test tool. I ran A/B tests for pricing and free trial screens. Lots of dashboards. Lots of SQL window functions. I loved the scale. Millions of users. You can feel the impact.
Pay was solid. The base range I saw for similar roles was $170k–$200k, plus bonus and some equity for full time folks. I was a contractor, so hourly was higher but with no equity. The commute from Eagle Rock to Glendale was fine. If I left after 4:30 PM, not so fine. Also, the interview process was long: recruiter chat, SQL live coding, a case, and then a panel with product and an engineer. Fair, but you need stamina. For a peek at how very corporate loops can feel, I once tried JPMorgan’s Aditi program—here’s my honest take if you’re curious.
Example 3: A game studio in West LA (my current team)
Now I’m with a game studio in West LA. Think live ops and ads. My title says Data Scientist, but I’m half Product Analyst and half ML. I help pick offers, tune ad frequency, and flag whales without spamming whales. We use Python, Snowflake, Looker, MLflow, and LightGBM. I ship small models, then test them with product folks.
The team does “on-call lite.” If a deploy breaks a key metric, we jump in. It’s not 2 AM, but sometimes it’s 7 AM. We have “experiment weeks” too. I ran a test on a new onboarding path. It moved 2.4% on day-1 retention. Not huge. But real.
Base comp ranges I’ve seen here are wide: $160k–$210k for mid to senior, with bonus and RSUs. Not FAANG-level equity, but it adds up. We’re hybrid. Three days in office. I’ll be honest: the 405 picks fights. I leave by 7:30 AM to keep my sanity. Tide over with a breakfast burrito, and I’m good.
A near-miss: Space things in Hawthorne
I also had a loop in Hawthorne with a space company. The tech screen was fair: SQL joins, time series, and a system design chat on data quality. Cool people. Cool work. But they needed full on-site, and I couldn’t swing that with family stuff. I passed. No hard feelings.
The good stuff
- Industry mix: You can work on TV, sports, music, ads, games, health, fintech, and even rockets. Bored? Pick a new story.
- Clear product work: A/B tests, funnels, and real user metrics. You see what moves the needle.
- Community: Meetups are friendly. People share decks and code. Slack groups help a lot. Groups like PyLadies LA host beginner-friendly nights, too.
- Weather + mood: Walks at lunch. Little chats outside. It sounds small. It’s not.
The gritty stuff
- Job titles are fuzzy: “Data Scientist” can mean analyst, ML engineer, or pipeline wizard. Ask for the actual duties.
- Long interview loops: Panel after panel. Take-homes are rare now, but case studies are common.
- Hybrid rules: Many teams want 2–3 days in office. Make sure the commute is sane.
- Cost of living: Pay helps, but rent bites. It’s LA.
My pay notes (what I saw)
- Early-career roles: $110k–$150k base, sometimes less at tiny startups, sometimes more with equity.
- Mid to senior: $150k–$210k base, plus bonus, RSUs, or both.
- Staff or lead: $200k–$260k base at bigger studios and streamers.
- Contractors: hourly can look high, but no equity and fewer perks.
These are ranges I saw or got, not a rulebook.
Common tools I touched
- Languages: Python and SQL, every day
- ML: scikit-learn, XGBoost, LightGBM; a bit of PyTorch when needed
- Data: BigQuery or Snowflake; Airflow for jobs; dbt for models
- Viz: Looker and Tableau
- ML ops: MLflow for tracking
- A/B testing: homegrown at big shops; third-party at smaller ones
If you’re torn between dashboard-heavy BI work and the more model-centric jobs I’m describing, my breakdown in Business Intelligence vs Data Science: My Hands-On Review might help clarify the trade-offs.
If you can write strong SQL and a clean notebook, you’re 70% there. If you can explain a metric shift to a PM without slides, you’re 90% there.
Where the jobs live
- Santa Monica, Culver City, Playa Vista: “Silicon Beach” stuff—streaming, ads, and marketplaces
- Burbank/Glendale: studios, media tech, and animation analytics
- West LA: gaming and ad tech
- Pasadena: research-y spots; some health and space
- DTLA: a mix—fintech, logistics, and civic data
Parking can be a pain near Santa Monica. Glendale is easier. Culver has both. Pick your battles.
What interviews felt like
Most loops had:
- SQL live coding (CTEs, window functions, edge cases)
- A case study (design an A/B test; explain bias; plan guardrails)
- A modeling chat (features, leakage, metrics)
- Product sense (what is success, and why?)
- A systems talk (data quality and pipeline checks)
I practiced on StrataScratch and LeetCode. I skimmed “Ace the Data Science Interview.” It helped, but the real jump came from doing one solid project with clean code, clear docs, and a small readme. People loved that.
Life stuff that matters
I measure time in songs, not minutes. From Highland Park to Playa Vista? That’s 8–12 songs, easy. From Culver to Santa Monica after 5 PM? That’s a podcast and a snack. Plan your radius. A 15-mile move can change your mood.
I also keep a “weekend brain” list. Hiking in Griffith, a taco stand in K-Town, and a nap. It makes the Monday standup less rough.
On that note, LA’s Asian neighborhoods—K