Snowflake Data Scientist

Role DescriptionSnowflake Data ScientistLocation – Richardson TX OnsiteRole OverviewWe are looking for Data Scientist to support the Americas Advisory Digital and Technology organization This is a handson 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 insight leadership can act on Speed rigor and communication matter as much as technical depthWhat You Will DoDesign 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 dataWrite and optimize complex SQL queries across PostgreSQL and Snowflake to support leasing research and market intelligence teams extracting transforming and validating data at scaleAnalyze datasets covering lease economics property hierarchies market comparables and transaction data to answer businesscritical questions with speed and accuracyBuild and deploy AIassisted analytical workflows using large language models including Claude and retrievalaugmented generation RAG patterns over structured and unstructured lease document corporaWork directly with senior leaders and business stakeholders to frame data problems present model findings and translate statistical output into plainlanguage recommendationsInvestigate data gaps and anomalies at the field level communicate root cause clearly and coordinate with the data platform team on resolution pathsApply rigorous data testing methodology including automated data quality checks dbt SODA Great Expectations to validate analytical outputs before they reach leadershipBuild and maintain analytical views dashboards and documentation that business teams can trust and act onIdentify patterns in data that surface risk opportunity or operational insight and frame those patterns in terms that drive leasing and market strategy decisionsRequiredWHAT YOU BRING4 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 environmentsExpertlevel SQL in both PostgreSQL and Snowflake including query optimization window functions and complex multitable joins across large datasetsProficiency in Python for data manipulation statistical modeling and automation pandas scikitlearn and similar libraries used in practice not just on a resumeHandson experience building and evaluating predictive models regression classification timeseries forecasting and anomaly detection applied to real business problemsWorking 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 upstreamHandson experience with AWS or another major cloud platform Azure GCP including cloudhosted data infrastructure S3 and managed compute servicesProven ability to work directly with senior leaders presenting findings with confidence educating stakeholders on methodology and fielding hard questions under pressureProficiency with AI tools including Claude to accelerate analysis automate repetitive tasks and improve turnaround on data requestsStrong written and verbal communication skills the ability to make model output and statistical findings accessible to nontechnical audiences without dumbing them downHigh sense of urgency able to hit the ground running with minimal rampup and deliver from day onePreferredExperience with large language model LLM integrations prompt engineering or RAG pipelines applied to documentheavy analytical workflowsFamiliarity with commercial real estate concepts including lease structures rent schedules break clauses market comparables and transaction economicsExperience with BI tools such as Sigma Computing Tableau or Power BI for presenting model outputs and analytical dashboardsFamiliarity with data testing frameworks dbt tests Great Expectations or SODA and a habit of building validation into the analytical process not bolting it on afterwardBackground supporting advisory research or transaction services teams within a CRE or financial services organizationExposure to vector databases embedding models or semantic search applied to document retrievalWhat Success Looks LikeLeadership gets accurate wellframed answers to data questions within hours not days backed by model output or validated SQL not gut feelPredictive models surface actionable signals leases at expiry risk tenants likely to churn marOther DetailsActual 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”):Benefits And PerksComprehensive Medical Plan Covering Medical, Dental, VisionShort Term and Long-Term Disability Coverage401(k) Plan with Company matchLife InsuranceVacation Time, Sick Leave, Paid HolidaysPaid Paternity and Maternity LeaveThe 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.