Founding Data Scientist

• You will be joining our tech team as the first Data Scientist at Inveno, a NYC based startup focused on automating manual resource planning in US Warehouses. Your primary responsibility will be to build the machine learning foundation that will revolutionize how resources are planned across thousands of warehouses in the US and optimize the processing of millions of packages. By harnessing operational data and developing custom AI and optimization models, you will enable warehouse managers to plan with precision, speed, and scale, thus eliminating inefficiencies and improving overall productivity.The founding team at Inveno consists of a warehouse operations expert with 8 years of experience at logistics startups, a CEO with a successful track record of building and exiting companies, and a CTO who has scaled engineering systems across various ventures internationally. The startup has already made significant progress, with the V1 product set to go live with the first client in 2 months and a paid contract secured with a global conglomerate.As a Data Scientist at Inveno, you will have the opportunity to lead the development and deployment of a foundational model that powers a critical sector. Your key responsibilities will include architecting and implementing the core optimization engine, building machine learning models using real-world warehouse data, collaborating with engineering to design workflows, and establishing MLOps best practices to ensure a robust and scalable ML infrastructure.To excel in this role, you must have experience in designing and deploying constraint-based optimization models using tools like Google OR-Tools, Gurobi, or CPLEX. Proficiency in Python and the data science stack is essential, along with a strong foundation in supervised ML techniques. You should be able to translate complex operational problems into solvable mathematical or ML models and hold a Bachelors or Masters degree in Computer Science, Operations Research, Statistics, or a related quantitative field.,

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model selection,constraintbased optimization models, pandas, scikitlearn, supervised ML, feature engineering, validation techniques

model selection,constraintbased optimization models, pandas, scikitlearn, supervised ML, feature engineering, validation techniques

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model selection,constraintbased optimization models, pandas, scikitlearn, supervised ML, feature engineering, validation techniques

model selection,constraintbased optimization models, pandas, scikitlearn, supervised ML, feature engineering, validation techniques

Data Engineering, ETL, Model Selection, Technical Leadership, Collaboration, Deployment, Operations, NumPy, AWS, Azure, GCP, R, Docker, Kubernetes,PySpark, Large Language Models LLMs, Data Pipelines, Feature Engineering, Hyperparameter Tuning, Model Evaluation, Pandas, Spark SQL, Shiny, MLOps

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