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About This Role
Who We Are
The Center for Geospatial Solutions (CGS) is a self\-sustaining nonprofit enterprise. Founded to help bridge the gap between policy and practice, our mission is to enable people and the planet to meet the pace of change by expanding access to new technologies that power more sustainable and equitable outcomes.
We are a fully remote team of award\-winning professionals with decades of applied expertise and end\-to\-end GIS capabilities. By embracing whole\-system thinking and state\-of\-the\-art technology, we enable partners across public, private, and nonprofit sectors to tackle complex, real\-world challenges—like housing affordability, ecosystem conservation, water management, and sustainable infrastructure—with greater clarity.
Our work liberates and connects key information, creates nuanced pictures of complex situations, and makes land, water, and social data easier to use, understand, and act on. We use tools like satellite data and artificial intelligence to deliver insights for impact.
At CGS, we believe that technology can be a tool for positive change. We are dedicated to building a diverse team that represents the communities and systems we live and work in. If you’re excited about this role but don’t meet every listed qualification, we encourage you to apply. We value potential, curiosity, and lived experiences, and know a more inclusive team makes us a stronger organization.
About the Lincoln Institute
CGS was established in 2020 at the Lincoln Institute of Land Policy, which seeks to improve quality of life through the effective use, taxation, and stewardship of land. A nonprofit private operating foundation whose origins date to 1946, the Lincoln Institute researches and recommends creative approaches to land as a solution to economic, social, and environmental challenges. Through education, training, publications, and events, the Lincoln Institute integrates theory and practice to inform public policy decisions worldwide and has office locations in Cambridge, Massachusetts; Washington, DC; Phoenix, Arizona; and Beijing, China.
Position Overview
=====================
The Center for Geospatial Solutions (CGS) is seeking a Data Scientist II to implement geospatial data science, remote sensing, and environmental modeling workflows that support real\-world choices about land, water, conservation, infrastructure, and related challenges.
Reporting to the Associate Director of Data Science, the Data Scientist will acquire and evaluate data, run and improve analytical workflows, conduct accuracy assessments, document results, and help automate repeatable processing steps. The role will exercise sound judgment about data quality, appropriate methods, and issues that should be elevated to senior technical staff.
This position is well suited to an applied data scientist who is comfortable moving between geospatial analysis, Python\-based processing, remote sensing, statistics, and collaborative project delivery. The successful candidate will be eager to deepen their expertise while producing reliable, reproducible work that can scale across projects and geographies.
What You Will Do
====================
Geospatial Analysis and Workflow Implementation
- Implement established geospatial data science and remote sensing workflows from data acquisition through processing, analysis, model execution, validation, and delivery.
- Acquire, organize, clean, and evaluate raster, vector, tabular, terrain, and Earth observation datasets from public, partner, and client sources.
- Use Python, GIS software, and open\-source tools to process large geospatial datasets and generate repeatable analytical outputs.
- Run geospatial machine learning or deep learning workflows, evaluate model outputs, and identify important performance issues or data limitations.
- Exercise judgment about the best available data, important caveats, and technical issues that should be elevated to senior technical staff.
Validation, Documentation, and Quality
- Conduct spatial statistics, accuracy assessments, quality\-control checks, and validation using established methods.
- Prepare clear documentation of data sources, processing steps, code, assumptions, limitations, and results.
- Review outputs for completeness, consistency, and technical quality before they are shared with partners or clients.
- Contribute to reproducible project structures, data dictionaries, metadata, and technical handoff materials.
Automation and Process Improvement
- Identify repetitive or error\-prone steps that can be automated, standardized, or made more efficient.
- Develop and maintain scripts, functions, notebooks, and reusable components that improve delivery speed and consistency.
- Work with senior data scientists, AI engineers, and cloud engineers to move useful prototypes toward scalable workflows.
- Test new datasets and methods and share practical findings with the broader technical team.
Collaboration and Delivery
- Work closely with senior technical staff, AI engineers, GIS professionals, project managers, and subject\-matter experts.
- Communicate progress, technical considerations, results, and risks clearly in team meetings, written updates, and presentations.
- Contribute to client deliverables, proposals, technical memos, presentations, and demonstrations.
- Participate in peer review and contribute to an inclusive team culture focused on learning, quality, and impact.
What You Will Need:
- Master’s degree or equivalent experience in data science, geography, remote sensing, environmental science, engineering, computer science, or a related quantitative field.
- 2\-5 years of professional experience applying geospatial analysis, data science, remote sensing, or environmental modeling to solve real world problems and address stakeholder needs
- Proficiency in Python for data engineering and data science workflows, including experience with common geospatial and scientific libraries.
- Experience with raster and vector data, GIS software such as Esri or open\-source equivalents, and large geospatial datasets.
- Strong foundation in statistics, data science, machine learning, and remote sensing.
- Experience acquiring, cleaning, evaluating, and documenting data from multiple sources.
- Experience independently designing and implementing GeoAI models and other analytical approaches, addressing open\-ended problems with incomplete or evolving requirements.
- Experience with Git/GitHub or comparable version\-control workflows.
- Strong attention to detail and commitment to reproducible, well\-documented work.
- Clear written and verbal communication skills and the ability to collaborate effectively in a fully remote environment.
- Collaborative and able to work successfully in interdisciplinary teams with colleagues from various topical backgrounds and different skill levels and communication levels
- Exceptional critical thinking skills with the ability to deconstruct complex problems, prioritize issues, and implement sensible solutions
- Experience translating client needs into clear milestones, scopes of work, and risk assessments
- Strong ability to communicate technical and computational concepts clearly to non\-technical audiences, including domain experts, stakeholders, and partners
- Self\-motivated and goal\-oriented with the ability to take an innovative, strategic, and evidence\-based approaches to research and empathetic collaboration
- Willingness and ability to learn new frameworks, data structures, and infrastructure
- U.S. Citizen, or legally authorized to work in the United States with no need for future sponsorship
Helpful Experience (Nice to Have):
- Experience with hydrology, geomorphology, terrain analysis, wetlands, ecology, or other environmental domains.
- Experience with cloud\-based processing using AWS, Azure, Google Cloud, Google Earth Engine, or similar platforms.
- Experience with geospatial foundation models, embeddings, or large\-scale Earth observation datasets.
- Experience contributing to academic or applied research, including study design, analysis, and communication of findings.
- Experience with field data, wetland delineations, survey data, or validation datasets.
- Experience supporting government, nonprofit, or consulting projects and working within defined scopes and deadlines.
Application Process
Please submit a cover letter and resume. The cover letter should succinctly describe your interest in joining the CGS team; why you are qualified; and what relevant expertise and experience you offer. Applications will be considered on a rolling basis until the position is filled.
Compensation Overview
The salary market range for this role is posted above and dependent on level of education and years of experience. We value internal and external equity and encourage those who may be missing qualifications to submit their materials still.
Our Benefits
Our Benefits Benefits highlights include but are not limited to (a) 3x employer contribution towards retirement matching your employee contribution up to 15%, (b) health insurance with no deductible (c) dental insurance, (d) vision insurance, (e) copay assistance through an employer\-funded health reimbursement account, (f) short\-term disability coverage, (g) long term disability coverage, (h) paid parental leave, (i) voluntary insurances such as accident insurance, (j) health care flexible spending, (k) dependent care flexible spending, (l) paid time off for holidays, vacation, personal, sick, bereavement, and jury duty, (m) office closure between December 24 Jan 1 each calendar year, (n) flexible schedule and option for a compressed 4 day workweek, (o) tuition and staff development reimbursement, (p) pet insurance, and (q) Employee Assistance Program.
Our Values
Cooperation and Teamwork, Forthright Feedback, Initiative, Acceptance of Responsibility, Multicultural Sensitivity
Equal Opportunity Employer
The Lincoln Institute of Land Policy is dedicated to creating an inclusive work environment by hiring, training, promoting, and carrying out personnel procedures with respect to compensation, benefits, transfers, layoffs, or terminations, on the basis of individual merit, experience, and ability without regard to race (including traits historically associated with race such as hair texture, length of hair, protective hairstyles or cultural or religious headdresses), color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), ancestry, citizenship status, gender identity or expression, genetic information, marital or domestic/civil partnership status, physical or mental disability, sexual orientation, veteran status, military service, serious medical condition, expunged juvenile record, personal appearance, family responsibilities, matriculation, political affiliation, status as a victim, credit information, homelessness status, reproductive health decision making, or any other characteristic protected by law or otherwise.
Pay Transparency Nondiscrimination Provision
Lincoln Institute of Land Policy will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
Non\-Smoking Organization
Lincoln Institute of Land Policy is a Non\-Smoking organization. Smoking and the use of tobacco products are prohibited at all times and on all property owned, leased, or under the control of Lincoln Institute of Land Policy at all times, including, but not limited to indoor and outdoor grounds, walkways and sidewalks, parking lots, company vehicles, and private vehicles parked on Lincoln Institute of Land Policy property.
MA Polygraph Statement
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
What You Will Need
- Master’s degree or equivalent experience in data science, geography, remote sensing, environmental science, engineering, computer science, or a related quantitative field.
- 2\-5 years of professional experience applying geospatial analysis, data science, remote sensing, or environmental modeling to solve real world problems and address stakeholder needs
- Proficiency in Python for data engineering and data science workflows, including experience with common geospatial and scientific libraries.
- Experience with raster and vector data, GIS software such as Esri or open\-source equivalents, and large geospatial datasets.
- Strong foundation in statistics, data science, machine learning, and remote sensing.
- Experience acquiring, cleaning, evaluating, and documenting data from multiple sources.
- Experience independently designing and implementing GeoAI models and other analytical approaches, addressing open\-ended problems with incomplete or evolving requirements.
- Experience with Git/GitHub or comparable version\-control workflows.
- Strong attention to detail and commitment to reproducible, well\-documented work.
- Clear written and verbal communication skills and the ability to collaborate effectively in a fully remote environment.
- Collaborative and able to work successfully in interdisciplinary teams with colleagues from various topical backgrounds and different skill levels and communication levels
- Exceptional critical thinking skills with the ability to deconstruct complex problems, prioritize issues, and implement sensible solutions
- Experience translating client needs into clear milestones, scopes of work, and risk assessments
- Strong ability to communicate technical and computational concepts clearly to non\-technical audiences, including domain experts, stakeholders, and partners
- Self\-motivated and goal\-oriented with the ability to take an innovative, strategic, and evidence\-based approaches to research and empathetic collaboration
- Willingness and ability to learn new frameworks, data structures, and infrastructure
- U.S. Citizen, or legally authorized to work in the United States with no need for future sponsorship
Helpful Experience (Nice to Have):
- Experience with hydrology, geomorphology, terrain analysis, wetlands, ecology, or other environmental domains.
- Experience with cloud\-based processing using AWS, Azure, Google Cloud, Google Earth Engine, or similar platforms.
- Experience with geospatial foundation models, embeddings, or large\-scale Earth observation datasets.
- Experience contributing to academic or applied research, including study design, analysis, and communication of findings.
- Experience with field data, wetland delineations, survey data, or validation datasets.
- Experience supporting government, nonprofit, or consulting projects and working within defined scopes and deadlines.
Application Process
Please submit a cover letter and resume. The cover letter should succinctly describe your interest in joining the CGS team; why you are qualified; and what relevant expertise and experience you offer. Applications will be considered on a rolling basis until the position is filled.
Compensation Overview
The salary market range for this role is posted above and dependent on level of education and years of experience. We value internal and external equity and encourage those who may be missing qualifications to submit their materials still.
Our Benefits
Our Benefits Benefits highlights include but are not limited to (a) 3x employer contribution towards retirement matching your employee contribution up to 15%, (b) health insurance with no deductible (c) dental insurance, (d) vision insurance, (e) copay assistance through an employer\-funded health reimbursement account, (f) short\-term disability coverage, (g) long term disability coverage, (h) paid parental leave, (i) voluntary insurances such as accident insurance, (j) health care flexible spending, (k) dependent care flexible spending, (l) paid time off for holidays, vacation, personal, sick, bereavement, and jury duty, (m) office closure between December 24 Jan 1 each calendar year, (n) flexible schedule and option for a compressed 4 day workweek, (o) tuition and staff development reimbursement, (p) pet insurance, and (q) Employee Assistance Program.
Our Values
Cooperation and Teamwork, Forthright Feedback, Initiative, Acceptance of Responsibility, Multicultural Sensitivity
Equal Opportunity Employer
The Lincoln Institute of Land Policy is dedicated to creating an inclusive work environment by hiring, training, promoting, and carrying out personnel procedures with respect to compensation, benefits, transfers, layoffs, or terminations, on the basis of individual merit, experience, and ability without regard to race (including traits historically associated with race such as hair texture, length of hair, protective hairstyles or cultural or religious headdresses), color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), ancestry, citizenship status, gender identity or expression, genetic information, marital or domestic/civil partnership status, physical or mental disability, sexual orientation, veteran status, military service, serious medical condition, expunged juvenile record, personal appearance, family responsibilities, matriculation, political affiliation, status as a victim, credit information, homelessness status, reproductive health decision making, or any other characteristic protected by law or otherwise.
Pay Transparency Nondiscrimination Provision
Lincoln Institute of Land Policy will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
Non\-Smoking Organization
Lincoln Institute of Land Policy is a Non\-Smoking organization. Smoking and the use of tobacco products are prohibited at all times and on all property owned, leased, or under the control of Lincoln Institute of Land Policy at all times, including, but not limited to indoor and outdoor grounds, walkways and sidewalks, parking lots, company vehicles, and private vehicles parked on Lincoln Institute of Land Policy property.
MA Polygraph Statement
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $76K-$95K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Lincoln Institute of Land Policy, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($85K) sits 56% below the category median. Disclosed range: $76K to $95K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Lincoln Institute of Land Policy AI Hiring
Lincoln Institute of Land Policy has 2 open AI roles right now. They're hiring across Data Scientist. Positions span Cambridge, MA, US, Washington, DC, US. Compensation range: $95K - $115K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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