Data Scientist I - Data Science & Machine Learning

Location: Austin, TX (Onsite) Contract Length: 6 Months (with extensions) Compensation: $59.50–$63.00/hr (W2 with benefits) Job Description We are seeking a Data Scientist to join an Intelligent Services team working with petabyte?scale machine and agronomic data to deliver production?ready analytics and machine learning solutions. This role partners closely with Product and Data Engineering teams to translate complex data into actionable insights that drive real?world outcomes. The ideal candidate is hands?on, technically strong, and able to clearly communicate findings to both technical and non?technical stakeholders. Education • MS preferred; PhD strongly preferred • Bachelor’s degree considered with strong applied data science experience Top Skills & Requirements • Production ML model development in Python (object?oriented) • Large?scale data processing with SQL, Spark, and Databricks • End?to?end ML or analytics solution delivery • KPI definition and performance measurement • Strong communication and collaboration skills • Comfortable working onsite in a cross?functional team Technical Skills • ML techniques: regression, supervised/unsupervised learning, probabilistic models, NLP • Data modeling and quality assessment (normalization, coverage, attribute analysis) • Model validation, bias detection, and drift monitoring • Visualization tools: Tableau, Kepler.gl, QGIS • Experience with structured, unstructured, time?series, and geo?tagged data Nice to Haves • Geospatial analysis (vector/raster data, geo?indexing) • Remote sensing, GIS, and satellite imagery • Computer vision (object detection, segmentation, SAM) • Advanced AI solutions (RAG, agentic systems, model fine?tuning, monitoring) • Additional languages (Java, JavaScript, Scala) • Simulation methods (Monte Carlo, Gibbs sampling) • Publications, patents, or project portfolio Key Responsibilities • Develop and deploy ML models using high?resolution machine and agronomic data • Translate analysis into actionable insights and recommendations • Define and track KPIs tied to customer and product success • Partner with Data Engineering on scalable analytics solutions • Communicate results, methodology, and trade?offs to stakeholders • Contribute to best practices for model development and monitoring #INDOEM