Our clients are facing disruption from venture-backed startups and new entrants like Amazon and Google. They have incredible data assets and niche operational expertise.What they don’t have is a small, high-performing team which can stand up their AI practice and get predictive models into operation. That’s where YOU come in — to help accelerate them into this new AI-driven world.If you’ve been working hard to build your skills in AI this is an opportunity to break into this amazing new field.
Your job is to build the machine learning models for our clients. You’ll be working in Jupyter Notebook, NumPy, Pandas, Python, and Pytorch.You know how to build traditional machine learning models like random forests. You can also build neural networks and know how to deal with overfitting. You’ve participated in several Kaggle contests and can share your results and code. You are comfortable doing EDAs and presenting results to clients.
You share our belief that the key to successful client engagements requires transfer learning. You like reading and learning about the latest AI techniques but you don’t want to be an AI researcher. You want to build AI models and solve real problems with the best solutions on the table. You read about emerging NLP techniques and be excited about putting them into practice.You are also interested in becoming a better programmer and learning the data engineering skills necessary to get your models into production.
Our clients are facing disruption from venture-backed startups and new entrants like Amazon and Google. They have incredible data assets and niche operational expertise.What they don’t have is a small, high-performing team which can stand up their AI practice and get predictive models into operation. That’s where YOU come in — to help accelerate them into this new AI-driven world.One of their biggest challenges is building the software engineering infrastructure required to get predictive models into a production environment. In fact, data engineer is now one of the hottest career fields. There is a bigger demand for data engineers than data scientists.
Your job will be designing the data pipelines and engineering infrastructure to support enterprise machine learning models. You will take the offline models data scientists build and turn them into a real system. You’ll be reviewing model results, helping develop new features, and otherwise building the infrastructure to support machine learning.
You are an experienced server-side software engineer who has enterprise-scale data processing infrastructure such as devops, custom ETL tools, job schedulers, and APIs.You’ve got opinions about test-driven development and OO design patterns — because you’ve got the scars from seeing it done badly.You’re excited about building rock-solid machine learning pipelines. You’ve heard about emerging tools like Airflow and look forward to trying them on client work.
Prolego (προλέγω, prolégo) means predict in Greek. In popular culture predictions are often considered informed guesses—in business, predictions can be the difference between success and failure. We use AI to help our clients make better predictions and decisions.