nibble

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
Deep Learning and Neural Networks

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 and Keras — LIME, SHAP — SpaCy, GenSim — HyperOpt

Cloud and DevOps

AWS (S3, EC2, Lambda, Redshift, RDS...)
Serverless computing
Containerization and orchestration
Continuous Integration and Continuous Delivery (CI/CD)

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