Data science: 
* research: advanced applied mathematics and statistics, hypothesis evaluation, digital signal processing, time series analysis, operations research, system analysis.
* modelling: classical ML (supervised and unsupervised), dimensionality reduction, optimisation, linear, non-linear and stochastic programming, control and decision systems engineering.
* other: deep learning, text mining, NLP, numerical analysis, data visualisation.

Stack: Scala, Java, Matlab, Python (numpy, scipy, pytorch, keras, tensorflow), Spark, Hadoop, Kafka, RDBMS, DWH, Elasticsearch.

Data architecture: big data and data science pre-sales and consulting, ML solution and pipelines design, API and DWH / data lake, data marts design (integration, data layout, cleansing, ingestion, governance).

Data engineering: ETLs, streaming and batch processing fault tolerant big data applications design (RDBMS, HDFS, Kafka, Spark, Spark Streaming, Akka, Akka Streams, BigQuery, Teradata), prototyping, implementation and deployment.

DevOps: cloud and hybrid connectivity for big data solutions on Azure, GCP, AWS, distributed applications deployment, provisioning best practices, logging and monitoring, CI/CD, Linux networking, Kubernetes.

Awareful enables mental health service providers to offer better care through data and artificial intelligence.

Tasks: data discovery, clustering and patterns extraction, data engineering, topological data analysis (non-linear dimensionality reduction).

System architecture: pre-sales and consulting, data architecture, API, DSL and protocols design, end-to-end applications architecture (mobile, frontend, backend, infrastructure, analytics, ML), DB layout design and scaling, fault-tolerant, HA system design.

Data engineering: streaming and batch big data pipelines (PubSub, Dataflow, Dataproc and BigQuery integration), DWH and data marts design, hybrid connectivity big data solutions design.

Server-side solutions: microservices and service-oriented architecture, NodeJS/TS and Java teams technical management.

Math: discrete optimization for TSP and knapsack problems with dynamic constraints, MIP techniques.

Cloud solutions: hybrid connectivity and cloud migration assistance, Google Cloud Platform and AWS infrastructure scaling and automation, GKE and bare-metal Kubernetes management, monitoring and logging (EFK, ELK, Stackdriver), CI/CD design (Gitlab, Jenkins, Cloud Builder), Cloud ML.