Introduction

Diving into the data job market! Focusing on data scientist roles, this project explores top-paying jobs, in-demand skills, and where high demand meets high slary in data science.

SQL Queries? Check them out here: project_sql folder

Background

Data comes from Luke Barousse's SQL For Data Analytics Course.

The questions I wanted to answer through my SQL queries were:

1. What are the top-paying data scientist jobs in Los Angeles?
2. What skills are required for these top-paying jobs?
3. What skills are most in-demand for data scientists?
4. Which skills are associated with higher salaries?
5. What are the most optimal skills to learn?

Tools I Used

For my dive into the data science job market, I used several tools:

• SQL: The backbone of my analysis, allowing me to query the database and discover important insights.
• PostgreSQL: The chosen database management system, ideal for handling this type of data.
• VS Code: My IDE of choice for programming and database management, as well as executing SQL queries.
• Git and GitHub: Essential for version control and sharing my SQL scripts and analysis.

Analysis

Each query for this project is aimed at inspecting key aspects of the data science job market.

1. Top Paying Data Scientist Jobs

To identify the highest-paying roles, I filtered data analyst positions by average yearly salary and location, focusing on remote jobs. This query highlights the high paying opportunities in the field.

SELECT 
    job_id,
    job_title,
    job_location,
    job_schedule_type,
    salary_year_avg,
    job_posted_date,
    company_dim.name AS company_name
FROM
    job_postings_fact
LEFT JOIN company_dim ON job_postings_fact.company_id = company_dim.company_id
WHERE
    job_title_short = 'Data Scientist' AND
    job_location LIKE '%Los Angeles%' AND 
    salary_year_avg IS NOT NULL
ORDER BY 
    salary_year_avg DESC
LIMIT 10;

💰 Salary Insights

• Average salary: ≈ $254K

• Highest: $282.5K at Snapchat

• Lowest: $225K at Riot Games

• Salary range is narrow, showing that top roles are highly competitive and senior-level (mostly manager, lead, or director).

Los Angeles’ top-paying data-science roles are:

• Heavily concentrated in tech and entertainment-adjacent industries

• Managerial or lead-level, blending data leadership, ML deployment, and product impact

• Best-compensated at companies linking AI + product personalization (Snapchat, Stripe) or AI + media (INgrooves, Riot Games).

2. Skills for Top Paying Jobs

To understand what skills are required for the top-paying jobs, I joined the job postings with the skills data, providing insights into what employers value for high-compensation roles.

WITH top_paying_jobs AS (
    SELECT 
        job_id,
        job_title,
        salary_year_avg,
        company_dim.name AS company_name
    FROM
        job_postings_fact
    LEFT JOIN company_dim ON job_postings_fact.company_id = company_dim.company_id
    WHERE
        job_title_short = 'Data Scientist' AND
        job_location LIKE '%Los Angeles%' AND 
        salary_year_avg IS NOT NULL
    ORDER BY 
        salary_year_avg DESC
    LIMIT 10
)

SELECT 
    top_paying_jobs.*,
    skills
FROM top_paying_jobs
INNER JOIN skills_job_dim ON top_paying_jobs.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id

Here’s an analysis of the skill distribution for the top 10 data scientist roles in Los Angeles:

• Python dominates all listings, appearing in nearly every role (10/10). It’s the primary language for modeling, data cleaning, and production-level pipelines.

• SQL is the second most common (8 mentions), emphasizing the need for strong database querying and data wrangling skills.

• Scikit-learn appears frequently (7 mentions), showing that classical machine learning and model deployment remain key requirements.

• R and TensorFlow tie (5 mentions each), reflecting dual needs — statistical modeling (R) and deep learning (TensorFlow).

• Overall, hiring trends suggest hybrid proficiency — Python + SQL + ML libraries — as the foundation for data scientists in LA, with bonus points for deep learning and statistical expertise.

3. In-Demand Skills for Data Scientists

This query helped identify the skills most frequently requested in job postings, directing focus to areas with high demand.

SELECT skills,
    COUNT(skills_job_dim.job_id) AS demand_count
FROM job_postings_fact
INNER JOIN skills_job_dim ON job_postings_fact.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
WHERE 
    job_title_short = 'Data Scientist'
GROUP BY
    skills
ORDER BY
    demand_count DESC
LIMIT 5;

Skill	Demand Count
Python	114,016
SQL	79,174
R	59,754
SAS	29,642
Tableau	29,513

• Python is by far the most dominant skill — appearing in more than 114K job listings, confirming its status as the universal language of data science.

• SQL follows closely, showing that data querying and pipeline work remain essential even in advanced AI roles.

• R continues to have strong relevance, especially for statistical modeling and research analytics.

• SAS and Tableau highlight ongoing demand for legacy enterprise tools and data visualization expertise in business-oriented roles.

Overall, the trend shows that technical versatility (Python + SQL + visualization) is the foundation of employability, while domain-specific tools (R, SAS, Tableau) offer specialization advantages.

4. Skills Based on Salary

Exploring the average salaries associated with different skills revealed which skills are the highest paying.

SELECT 
    skills,
    ROUND(AVG(salary_year_avg), 0) AS avg_salary
FROM job_postings_fact
INNER JOIN skills_job_dim ON job_postings_fact.job_id = skills_job_dim.job_id
INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
WHERE 
    job_title_short = 'Data Scientist' AND
    salary_year_avg IS NOT NULL 
GROUP BY
    skills
ORDER BY
    avg_salary DESC
LIMIT 25;

Skill	Avg Salary ($)
Asana	215,477
Airtable	201,143
Red Hat	189,500
Watson	187,417
Elixir	170,824

• Asana ($215K) – Indicates strong value placed on data science managers and team leads who excel in agile project management and workflow orchestration within data teams.

• Airtable ($201K) – Reflects the rise of hybrid low-code platforms that allow for rapid data prototyping, automation, and collaborative analytics — bridging technical and business roles.

• Red Hat ($189K) – Signals high demand for engineers skilled in model deployment and infrastructure within enterprise Linux and open-source environments.

• Watson ($187K) – Highlights the premium on enterprise-grade AI and NLP expertise, particularly for large-scale corporate AI integrations in sectors like healthcare and finance.

• Elixir ($171K) – Suggests that proficiency in scalable, concurrent backend development for AI and data-heavy systems is highly rewarded.

Overall Insight: High-paying skills in 2025 emphasize AI operations, infrastructure, and integration — not just data modeling. The top earners are those who can connect data workflows, automation tools, and scalable systems across the organization.

5. Most Optimal Skills to Learn

Combining insights from demand and salary data, this query aimed to pinpoint skills that are both in high demand and have high salaries, offering a strategic focus for skill development.

WITH skills_demand AS (
    SELECT
        skills_dim.skill_id, 
        skills_dim.skills,
        COUNT(skills_job_dim.job_id) AS demand_count
    FROM job_postings_fact
    INNER JOIN skills_job_dim ON job_postings_fact.job_id = skills_job_dim.job_id
    INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
    WHERE 
        job_title_short = 'Data Scientist'
        AND salary_year_avg IS NOT NULL 
        AND job_location LIKE '%Los Angeles%'
    GROUP BY
        skills_dim.skill_id
), average_salary AS (
    SELECT
        skills_job_dim.skill_id, 
        ROUND(AVG(salary_year_avg), 0) AS avg_salary
    FROM job_postings_fact
    INNER JOIN skills_job_dim ON job_postings_fact.job_id = skills_job_dim.job_id
    INNER JOIN skills_dim ON skills_job_dim.skill_id = skills_dim.skill_id
    WHERE 
        job_title_short = 'Data Scientist' AND
        salary_year_avg IS NOT NULL 
        AND job_location LIKE '%Los Angeles%'
    GROUP BY
        skills_job_dim.skill_id
)


SELECT 
    skills_demand.skill_id,
    skills_demand.skills,
    demand_count,
    avg_salary
FROM
    skills_demand
INNER JOIN average_salary ON skills_demand.skill_id = average_salary.skill_id
WHERE
    demand_count > 10
ORDER BY
    avg_salary DESC,
    demand_count DESC
LIMIT 25;

Skill	Demand Count	Avg Salary ($)
PyTorch	11	209,045
Scikit-learn	11	207,773
TensorFlow	14	195,149
AWS	14	185,041
Spark	15	175,507

• PyTorch ($209K) – Represents the gold standard for deep learning development, particularly in research-driven AI roles. Its combination of high pay and demand suggests employers value practical deployment experience with neural architectures.

• Scikit-learn ($207K) – Shows that classical machine learning remains essential. Companies reward those who can efficiently use proven algorithms for production-ready solutions — not just experimental models.

• TensorFlow ($195K) – Highlights ongoing corporate reliance on Google’s ML ecosystem, particularly in large-scale model training and production pipelines.

• AWS ($185K) – Demonstrates that cloud computing proficiency is central to scaling AI workloads. The premium reflects the need for data scientists who can integrate ML systems with cloud-based infrastructure.

• Spark ($175K) – Underscores demand for big data processing and distributed computing. Professionals who can handle data at scale — bridging data engineering and ML — remain highly compensated.

Overall Insight: Top-tier skills blend AI frameworks (PyTorch, TensorFlow) with infrastructure expertise (AWS, Spark). The market increasingly rewards data scientists who can both build and operationalize models, emphasizing versatility over specialization.

What I Learned

Throughout this adventure, I leveled up my SQL and data analysis skills with some serious upgrades:

🧩 Complex Query Crafting: Mastered multi-table joins, refined WITH clauses (CTEs), and learned how to blend datasets to uncover deep relationships across job postings, salaries, and skills.

📊 Analytical Aggregation: Got fluent with GROUP BY, COUNT(), and AVG() — transforming raw job data into meaningful patterns about skill demand and salary trends.

⚙️ Real-World Application: Moved beyond textbook queries to design analytical SQL that mirrors real business questions — identifying where high demand meets high pay in data science.

💡 Data Storytelling: Turned SQL outputs into insights and visuals that explain not just what the numbers say, but why they matter in the modern data job market.

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Conclusions

🔍 Insights

From this analysis, several key insights emerged about the data science job landscape in 2025:

• Top-Paying Data Scientist Jobs: The highest-paying roles in Los Angeles average around $254K, concentrated in tech and entertainment industries like Snapchat, Stripe, and Riot Games.
• Skills for Top-Paying Jobs: Mastery of Python, SQL, and Scikit-learn forms the backbone of high-paying data science positions, with deep learning and leadership skills boosting earnings further.
• Most In-Demand Skills: Python dominates in demand, but SQL remains the indispensable skill that every data scientist needs to handle and query data efficiently.
• Skills with Higher Salaries: Tools like Asana, Airtable, and Red Hat command higher average salaries, suggesting companies reward those who can integrate AI with workflow and infrastructure.
• Optimal Skills for Market Value: PyTorch, TensorFlow, AWS, and Spark balance both high salary and demand — ideal targets for data scientists aiming to stay competitive in a rapidly evolving field.

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Closing Thoughts

This project didn’t just sharpen my SQL skills — it connected technical analysis with real-world career insight.
By exploring thousands of job postings, I gained a clearer view of what truly drives success in data science:

• Strong fundamentals (Python, SQL, ML frameworks)
• Infrastructure know-how (AWS, Spark)
• Strategic problem-solving

Ultimately, this exploration proved that data science isn’t just about building models — it’s about bridging insights, automation, and business value.
The journey reinforced one powerful takeaway: the best data scientists never stop learning, adapting, and connecting the dots between data and decision-making.