Data Scientist Contract Commercial Real Estate Analytics

Role Description

We are looking for a Data Scientist to support the Americas Advisory Digital and Technology organization. This is a hands-on, high visibility engagement working directly with leadership and business stakeholders across leasing research and market intelligence. You will own the full analytical stack from writing SQL to profiling raw lease data to building predictive models and AI-powered solutions that surface insights leadership can act on. Speed, rigor, and communication matter as much as technical depth.

What You Will Do

• Design, build, and validate predictive models covering lease expiry risk, rent trajectory, tenant retention probability, and market demand signals using structured and unstructured commercial real estate data.
• Write and optimize complex SQL queries across PostgreSQL and Snowflake to support leasing research and market intelligence teams, extracting, transforming, and validating data at scale.
• Analyze datasets covering lease economics, property hierarchies, market comparables, and transaction data to answer business-critical questions with speed and accuracy.
• Build and deploy AI-assisted analytical workflows using large language models including Claude and retrieval-augmented generation (RAG) patterns over structured and unstructured lease document corpora.
• Work directly with senior leaders and business stakeholders to frame data problems, present model findings, and translate statistical output into plain-language recommendations.
• Investigate data gaps and anomalies at the field level, communicate root cause clearly, and coordinate with the data platform team on resolution paths.
• Build and maintain analytical views, dashboards, and documentation that business teams can trust and act on.
• Identify patterns in data that surface risk, opportunity, or operational insight and frame those patterns in terms that drive leasing and market strategy decisions.

Required

• 4 to 8 years of experience in data science or advanced analytics with meaningful exposure to commercial real estate, financial services, or similarly complex transactional data environments.
• Expert-level SQL in both PostgreSQL and Snowflake, including query optimization, window functions, and complex multitable joins across large datasets.
• Proficiency in Python for data manipulation, statistical modeling, and automation, including pandas, scikit-learn, and similar libraries used in practice, not just on a resume.
• Hand-on experience building and evaluating predictive models, regression, classification, timeseries forecasting, and anomaly detection applied to real business problems.
• Working knowledge of ETL and CDC concepts, understanding how data flows from source systems into a cloud data warehouse and how to trace data quality issues upstream.
• Hand-on experience with AWS or another major cloud platform (Azure, GCP) including cloud-hosted data infrastructure, S3, and managed compute services.
• Proven ability to work directly with senior leaders, presenting findings with confidence, educating stakeholders on methodology, and fielding hard questions under pressure.
• Proficiency with AI tools including Claude to accelerate analysis, automate repetitive tasks, and improve turnaround on data requests.
• Strong written and verbal communication skills, the ability to make model output and statistical findings accessible to nontechnical audiences without dumbing them down.
• A high sense of urgency, able to hit the ground running with minimal ramp-up and deliver from day one.

Preferred

• Experience with large language model LLM integrations, prompt engineering, or RAG pipelines applied to document-heavy analytical workflows.
• Familiarity with commercial real estate concepts including lease structures, rent schedules, break clauses, market comparables, and transaction economics.
• Experience with BI tools such as Sigma Computing, Tableau, or Power BI for presenting model outputs and analytical dashboards.
• Familiarity with data testing frameworks like dbt, tests, Great Expectations, or SODA and a habit of building validation into the analytical process, not bolting it on afterward.
• Background supporting advisory research or transaction services teams within a CRE or financial services organization.
• Exposure to vector databases, embedding models, or semantic search applied to document retrieval.

What Success Looks Like

Leadership gets accurate, well-framed answers to data questions within hours, backed by model output or validated SQL, not gut feel. Predictive models surface actionable signals, such as leases at expiry risk, tenants likely to churn, market trends, etc.

Other Details

Actual compensation within the range will be dependent upon the individual's skills, experience, performance, and internal equity. Benefits/perks listed below may vary depending on the nature of your employment with LTIMindtree ("LTIM").

• Comprehensive Medical Plan Covering Medical, Dental, Vision, Short Term and Long-Term Disability Coverage
• 401(k) Plan with Company match
• Life Insurance
• Vacation Time, Sick Leave, Paid Holidays
• Paid Paternity and Maternity Leave

The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job-related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation like an annual performance-based bonus, sales incentive pay, and other forms of bonus or variable compensation.

Disclaimer: The compensation and benefits information provided herein is accurate as of the date of this posting. LTIMindtree is an equal opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family-care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectional or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.