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About This Role
ABOUT CIM GROUP: CIM is a community-focused real estate and infrastructure owner, operator, lender, and developer. Our team of experts works together to identify and create value in real assets, benefiting the communities in which we invest. Back in 1994, our three founders focused on projects in Southern California neighborhoods. Today, we are a diverse team of 900+ employees with projects across the Americas. Our projects have delivered jobs; created comfortable places to live, work, and relax; and provided necessary and sustainable infrastructure. Our focus on enhancing communities is unwavering, and we strive to make an even greater impact in the years to come. Join us and make an impact today! POSITION PURPOSE:
The Senior Data Scientist will be a pivotal member of our Enterprise Data Management team, playing a critical role in transforming raw, complex data into actionable intelligence that drives superior investment outcomes and operational efficiencies across the firm and its portfolio companies. The role is strategically positioned at the intersection of deep financial domain expertise and cutting-edge data science, requiring a proactive approach to problem-solving and the ability to translate complex technical findings into clear, strategic recommendations for investment teams and senior leadership. In this role, you will bridge the gap between advanced predictive modeling and production engineering, owning the full model lifecycle from feature engineering and offline experimentation to containerized deployment, CI/CD automation, and post-deployment monitoring. The work will directly contribute to, and be enabled by, the firm’s foundational data assets and policies.
RESPONSIBILITIES:
- Conduct in-depth analysis of large datasets to identify trends, patterns, and insights that can inform business strategy.
- Translate complex findings into clear, actionable recommendations.
- End-to-end machine learning delivery, including architecting, training, evaluating, and deploying production-grade predictive models and statistical algorithms across vast, disparate datasets.
- Design and implement sophisticated AI-powered algorithms and predictive models to continuously monitor, analyze, and optimize company performance across all three investment platforms.
- Model governance & monitoring; implement automated monitoring for production models to detect data drift, concept drift, feature skew, and latency degradation. Establish retraining triggers and model registry governance.
- Own the deployment and operationalization of ML models using MLflow, containerization (Docker), and automated CI/CD pipelines. Transition models from notebook prototypes into scalable batch jobs or low-latency REST APIs.
- Continuously refine search parameters, algorithms, and recommendations based on historical deal patterns, dynamic market conditions, and emerging sectors.
- Contribute to the design of scalable data pipelines and data lakes that power advanced analytics and AI applications.
- Champion best practices for data protection, including encryption and access controls, to safeguard sensitive information.
- Collaborate to define and implement robust data quality standards, ensuring all analytical models are built on a foundation of reliable data.
- Operate within our compliance framework to ensure ethical data handling, regulatory compliance, and consistency across the enterprise.
- Work with business stakeholders, data engineers, data stewards, and information architects to ensure data quality and accuracy of analytics and reporting.
EDUCATION/EXPERIENCE REQUIREMENTS: (including certification, licenses, etc.)
Required:
- Master’s or Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Quantitative Finance, or a closely related quantitative field.
- 5+ years of dedicated experience as a Data Scientist.
- Expert-level Python and strong SQL skills, with experience using NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch, and SQL databases such as PostgreSQL or T-SQL. (R, MATLAB, C++, Java, or C# are a plus.)
- Deep practical expertise with tree-based algorithms and gradient boosting (XGBoost, LightGBM, CatBoost) on structured/tabular business and financial data.
- Strong foundational statistics, including hypothesis testing, regression analysis, regularized models, classification, and time-series forecasting.
- Strong understanding of MLOps solutions for model deployment, management, and scaling (e.g., SageMaker, Vertex AI, Azure ML, MLflow, Docker).
- Proven experience managing the full model lifecycle using MLflow, including experiment tracking, model registry, and artifact storage.
- Wide range of ML algorithms, including k-NN, Naive Bayes, SVM, Decision Forests, Boosting Algorithms, Deep Learning, Time Series Forecasting, NLP, and Reinforcement Learning.
- Ability to work effectively in a dynamic, team-oriented environment.
Preferred:
- Relevant professional certifications such as Chartered Financial Analyst (CFA) or Financial Risk Manager (FRM).
- Strong working knowledge of one of the following:
Tableau, Power BI, D3.js, Matplotlib, Seaborn, or ggplot.
- Familiarity with Git-based CI/CD workflows and automated testing for data science repositories.
ABOUT YOU:
- Excellent communication skills to articulate complex data concepts to non-technical stakeholders.
- Ability to build strong relationships across departments to ensure collaborative data initiatives.
- Pragmatic problem solver; prioritize robust, explainable baseline models (like tuned gradient boosted trees) over unnecessary complexity, scaling up architecture only when the business problem demands it.
- Strong analytical and problem-solving skills to innovate and drive improvements in data processes are required.
WHAT CIM OFFERS: At CIM, we believe our success stems from our collective efforts, and we are committed to providing well-rounded support and resources for our employees. In addition to a competitive compensation plan, CIM offers a comprehensive benefits program for employees to thrive both inside and outside of work. Eligible employees can enjoy a wide range of benefits, including:
- A variety of Medical, dental, and vision benefit plans
- Health Savings Account with a generous employer contribution
- Company paid life and disability insurance
- 401(k) savings plan, with company match
- Comprehensive paid time off, including: vacation days, 10 designated holidays, sick time, and bereavement leave
- Up to 16 hours of volunteer time off
- Up to 16 weeks of Paid Parental Leave
- Ongoing professional development programs
- Wellness program, including monthly and quarterly prizes
- And more!
Actual base salary considers several factors including but not limited to geography, job-related knowledge, experience, and budget. The start of the salary range is typically associated with the minimum experience required. At CIM, base pay is one part of the total compensation package. For this role, bonus compensation may be a significant part of the total compensation. The anticipated base salary range for the position in Los Angeles, CA is $150,000 - $175,000.
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HOW WE FEEL ABOUT DIVERSITY AND INCLUSION: At CIM Group, we believe that the unique perspectives and backgrounds of our employees enhance everything we do. We are committed to fostering an inclusive environment where diversity is not only respected but celebrated. We strive to ensure that our workplace is free from discrimination and harassment, allowing everyone to contribute meaningfully and feel a sense of belonging. As an equal opportunity employer, we strictly prohibit any form of unlawful discrimination and adhere to the laws enforced by the EEOC. Our goal is to provide a safe and supportive environment where all employees can grow and make impactful contributions together.
- Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on CIM Group. Please inform our Talent team if you need any assistance completing any forms or to otherwise participate in the application process.
CIM is committed to maintaining the confidentiality and privacy of your personal and financial information. Please click here for our Privacy Policy.
CIM does not accept unsolicited resumes from Agencies. Any unsolicited resumes received from Agencies will be considered property of CIM and no fees will be due or paid. If you wish to become an approved Agency with CIM or any of its Affiliates, please contact a member of the CIM Talent Acquisition Team.
Salary Context
This $150K-$175K range is below the median for Data Scientist roles in our dataset (median: $167K across 172 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At CIM GROUP, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($162K) sits 14% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
CIM GROUP AI Hiring
CIM GROUP has 1 open AI role right now. They're hiring across Data Scientist. Based in Los Angeles, CA, US. Compensation range: $175K - $175K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,340 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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