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About This Role
Citian is a fast growing, venture backed SaaS technology company based in Washington, DC. Our software solutions revolutionize how our transportation and infrastructure systems – roads, rail, transit, bicycle, pedestrian – operate.
Our tech solutions:
- Reduce traffic fatalities
- Enhance pedestrian accessibility
- Empower system operators to save time \& money
You'll work with some of the brightest minds in the software and transportation industries. Our engineers and data scientists apply the latest in emerging tech, Artificial Intelligence, and Machine Learning to build smarter, more advanced tools for our diverse client base. We work with clients across the United States, with global ambitions in the years ahead.
Who You Are:
Citian is seeking a Transportation Data Scientist who will play a crucial role in developing and implementing state\-of\-the\-art machine learning models for use cases in traffic safety, mobility, and city planning. The ideal candidate will exhibit excellent knowledge of the data science and data engineering disciplines with transportation industry experience, including working with large datasets in a Python/SQL environment. You have at least 3\+ years of data science experience and NLP. You will collaborate with cross\-functional teams to enhance our products and services, driving innovation and providing actionable intelligence to meet business goals. If you are passionate about data and are eager to contribute to the success of a startup, we would love to hear from you.
Responsibilities:
- Model Development: Develop and optimize data science models to process, analyze, and extract information from varying data sources, particularly textual.
- Machine Learning and AI: Apply machine learning and artificial intelligence techniques to build predictive and prescriptive models.
- Data Preprocessing: Clean, preprocess, and transform large datasets to prepare them for analysis and model training.
- Feature Engineering: Identify and engineer relevant features to enhance model performance and accuracy.
- Model Deployment and Evaluation: Design and implement robust evaluation metrics and frameworks to assess and monitor the performance of machine learning models.
- Collaboration: Work closely with cross\-functional teams, including engineers, product managers, and domain experts, to understand business requirements and deliver data science solutions that meet those needs.
- Research: Stay updated on the latest advancements in NLP and AI research and apply them to real\-world problems as needed.
- Embrace the startup mentality by wearing multiple hats and stepping outside traditional job roles as needed. Your versatility and willingness to take on diverse responsibilities will be key to our success in a dynamic and evolving landscape.
Qualifications:
- Bachelor's in Computer Science, Data Science, or a related field
- 3\+ years of experience in machine learning, artificial intelligence, and data science ideally within the transportation industry
- Strong knowledge of Python, SQL, Python data science libraries (Pandas, Numpy, Scikit\-learn, etc.), and Python NLP libraries (NLTK, SpaCy, etc.)
- Strong knowledge of machine learning techniques, NLP and AI algorithms
- Experience working with geospatial data (SQL PostGIS, Python GeoPandas, ArcToolbox Deep Learning, etc.)
- Experience with transformers and LLMs (HuggingFace, GPT, PyTorch, TensorFlow, etc.) and cloud infrastructure platforms such as Snowflake and AWS
- Demonstrated ability to work in a collaborative team environment
- Excellent communication and data storytelling skills
- Strong problem\-solving and analytical skills
Your Citian Advantage:
- Competitive salary and benefits package including medical, dental, and vision insurance and generous PTO.
- 401(k) company match and monthly commuter transportation benefit
- On\-site gym and free snacks in the office
- High\-growth potential and opportunities for advancement within a fast growing, venture\-backed SaaS startup!
Let's build smarter cities together! Learn more about Citian here: www.citian.co
\*\* Citian is an organization committed to diversity and inclusion to drive our business results and create a better future every day for our diverse employees, clients, partners, and communities. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, or protected veteran status and will not be discriminated against on the basis of disability.
Equal Opportunity/Affirmative Action Employer Minorities/Females/Protected Veterans/Persons with Disabilities
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Citian, 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Citian AI Hiring
Citian has 1 open AI role right now. They're hiring across Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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