Spanish for meditative and contemplating, Cogitativo was created with the purpose of thinking deeply about the issues affecting our healthcare system.

WE ARE A DIFFERENT KIND OF DATA SCIENCE COMPANY

We are singularly focused on improving our healthcare system for all stakeholders – especially patients - by helping our clients make sense of complex data answering their most challenging questions.

Trained and educated at some of the world's top academic institutions, our data science team uses advanced analytical and machine learning methods. These methods, combined with nationally recognized healthcare operators, provide our clients unbiased, scientific insights into their operational performance. These insights enable our clients to understand actionable opportunities for improving systems of care and healthcare affordability.

Cogitativo is fast-paced, and in this role, you'll be expected to make decisions independently by weighing many needs and priorities. The role and its surrounding organization aren't rigidly defined; successful candidates will excel in a creative, fast-paced, results-oriented environment. We are looking for mission-driven teammates who can jump into any situation with enthusiasm and deliver on tight timelines.

As Machine Learning Engineer here is what you will do:

• Collaborate closely with the R&D Data Science team on data exploration to build engineering rigor into our machine learning and deep learning processes.
• Guide the team in the evaluation of our existing machine learning architectures, and design new novel performant and scalable machine learning architectures.
• Leverage technical expertise to build out our ML pipelines by selecting the right tools for the job, including unexplored new machine learning tools, or packages currently in use like H20 Python Package, SQL Alchemy, Scikit Learn, PyTorch, Keras.
• Work extensively on model optimization, and support the Chief of Data Science’s performance engineering efforts.
• Benchmark existing machine learning pipelines to identify and fix bottlenecks.
• Integrate new models into the pipeline, perform exploratory data analysis, and build new model features.
• Write complex functional code in Python for various data initiatives, and perform code reviews of other contributors with emphasis on software development best practices.

Qualifications and Requirements

• Master’s or PhD in Computer Science, Computer Engineering, ML Engineering or other related quantitative disciplines.
• Domain knowledge of health care data sets (provider & payor) and clinical healthcare terminology.
• Exposure to EHR and claims data is relevant, like labs and vitals, medical and pharmacy claims, membership, eligibility, and provider data.
• Proven track record of leading teams through technical challenges to deliver ML pipelines and solutions at scale.
• Strong programming experience in Python, or intermediate Python skills with REST API design skills in Scala, Java or C++.
• Strong work experience with common machine learning libraries and tools: scikit-learn, H2O, MLFLow, Tensorflow.
• Understanding of foundational machine learning models and concepts: regression, random forest, XGBoost, SVM, HMM, CRFs, MRFs, deep learning.
• Some experience with large-scale data platforms, including hardware tech stack and architecture is an asset
• Experience writing complex functional code for batch, streaming and real-time data processing technology stacks like AWS Redshift, Microsoft Azure, Databricks, Spark, Hadoop, NoSQL databases, various data-storage systems in a Linux-based environment.
• Ability to thrive and demonstrate constant applied learning in highly complex, interdisciplinary, and dynamic work environment.
• Industry experience working across different organizations, ranging in size, from start-ups to large corporations is a plus