Data is eating the world.

And we'd love to help you to take a big bite.

We believe in taking ownership.

We approach Advanced Analytics through their full lifecycle, that doesn't mean we don't care about algorithms and theorical concepts —these still matter a lot— but we understand that these only deliver value in a complex entangled pipeline. Machine Learning is powerful, but if we figure out a way to improve your bottom line by 10% with just a smart SQL query , we will do it.

Domains

Data Strategy and design thinking

  • Define use cases and find biggest value projects
  • Understanding of customer needs to make sure we will hit the target and deliver values

Data Engineering

  • ETL pipelines
  • Model serving: Flask, ONNX, FastAPI, Serverless
  • Automated testing
  • PostgreSQL, MySQL, Oracle
  • Redis, ElasticSearch

Favorite tools

Scala, Python — Spark, Airflow - Flask, ONNX, FastAPI - AWS Lambda - AWS Codebuild - pytest

Data Science

  • Exploratory Data Analysis and Data Mining
  • Operations research
  • Statistics: hypothesis testing, statistical modeling
  • Algorithmic: graphs and network models
  • Machine Learning: classification, regression, clustering, segmentation, and topic modeling, anomaly detection, survival analysis, recommender systems, time series, model explainability

Favorite tools

Python and Jupyter notebooks — NumPy and pandas — Matplotlib and Seaborn — SciPy and StatsModels — NetworkX and PyMC — Scikit-Learn — Spark and SparkML — Tensorflow 2 — LIME, SHAP — SpaCy, GenSim — HyperOpt

Cloud and DevOps

  • AWS (S3, EC2, Lambda, Redshift, RDS...)
  • Serverless
  • Containerization and orchestration
  • Continuous Delivery

Favorite tools

Shell scripting, Python — Docker, Kubernetes — CircleCI

Misc.

  • Design and creation of User Interfaces
  • Frontend and backend development
  • Webscrapping and REST APIs

Favorite tools

Python and javascript — Django, Flask, Tornado — Vue.js — scrapy, requests, BeautifulSoup — asyncio

Contact us

[email protected]