Machine Learning Engineer / Data Scientist (Hybrid)

Salary: 100000.00 – 125000.00

Machine Learning Engineer / Data Scientist Austin, TX (Must be local) 6-12 months Contract (may go for long term) THE ROLE: This ML Engineer role will support client’s world-wide software engineering teams in their efforts to properly and accurately forecast resource requirements for software delivery of programs. You will interface with representatives from all software domains to understand the key technologies that drive complexity when developing software, gather and analyze past software development effort data, and create an AI model based on a suitable machine learning framework that will automatically generate resource requirement forecasts, based on the supplied program parameters. THE PERSON: The individual is expected to work independently, yet in a collaborative fashion, be self-motivated, task orientated and have excellent written and verbal communication skills. You are detail-oriented, you plan, implement, evaluate the work, and strive to improve with every iteration. You have strong analytical and problem-solving skills to understand complex data sets and derive actionable insights. KEY RESPONSIBILITIES: Develop, train, and evaluate machine learning models for program complexity categorization and resource forecasting. Implement and integrate models into the forecasting system. Optimize model performance and ensure accuracy. Extract and prepare data for model training and analysis. Collect, clean, and analyze data for program complexity estimation and forecasting. Develop and implement data analysis methods for determining program complexity drivers. Conduct meta-analysis studies to estimate actual work effort based on program complexity. Collaborate with cross-functional teams to ensure project success. Strong analytical and problem-solving skills to understand complex data sets and derive actionable insights. PREFERRED EXPERIENCE: 3+ years of combined experience in machine learning model development, data analysis, and statistical modeling. Strong understanding of machine learning algorithms, model evaluation metrics, optimization techniques, and statistical analysis techniques. Demonstrated ability to implement, validate, and improve supervised and unsupervised learning models. Excellent knowledge of Excel, Python, SQL, and database systems. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or related frameworks. Experience in evaluation metrics, model selection, and validation techniques such as cross-validation, bootstrapping, and assessing the statistical significance of model improvements. Excellent knowledge of data analysis, visualization, and preprocessing techniques. Familiarity with tools such as Pandas, NumPy, and Matplotlib. Experience deploying Classical and Modern Time Series Forecasting solutions using tools such as Neural Prophet, SARIMA, Chronos, etc Familiar with cloud-based machine learning platforms such as AWS Sagemaker, Google AI Platform, or Azure Machine Learning for model training, deployment, and monitoring. Good understanding of software development principles including version control (e.g. Git), Agile methodologies, and DevOps practices. Compensation: $100K-125K/annum ACADEMIC CREDENTIALS: Master’s degree in Computer Science, Statistics, Data Science, or related field.