Position: Data Analyst
Company: Waverley Software
Experience required: 2y
English level: intermediate

We are looking for a team member with experience in data analysis, data science, EDA, and/or machine learning.

Our client helps companies keep their people and operations secure with Micro-Intelligence, or street-level threat assessments. Using AI to aggregate public and proprietary information, the product has created the world’s largest dataset of global threat patterns. A visualization platform helps companies conduct geolocation-based analysis for travel safety, threat intelligence, crisis management, and site selection and expansion.


KEY RESPONSIBILITIES
● Work with the product team & customers to understand user needs, define requirements and deliver data-driven insights that support platform use cases.
● Triage issues and customer requests, perform analysis, and facilitate development work with the engineering and data science teams.
● Wrangle and analyze spatiotemporal data to identify patterns and trends, including exploratory data analysis, report creation, and supporting product discovery efforts.
● Organize, define processes, and coordinate data research to collect, analyze and assess threat datasets internationally for curation and inclusion in the Base Operations platform
● Support data ingestion pipelines through issue triage, investigation, pattern detection and troubleshooting
● Support development, evaluation and further enhancement of Machine Learning and NLP models.
● Maintain and improve the documentation used for the labeling campaign. Participate in the labeling campaign, and help to coordinate the team of analysts and interns involved in the project, resolve any issues related to the labeling.
● Working knowledge of AWS cloud service (EC2, S3, RDS, OpenSearch, Redshift, ECS, CloudWatch, etc.)

TECHNOLOGIES WE USE
● Languages: Python (Numpy, Pandas, GeoPandas, plotly, seaborn), SQL
● Tools: AWS, Git, Docker, Kubernetes, Swagger/OpenAPI
● Datastores: Postgres (with POSTGIS), S3, Elasticsearch
● NN frameworks/libraries: Spacy, Tensorflow/Keras (Transformers, LSTMs, CRFs)
● Conventional DS frameworks/libraries: LightGBM, XGBoost, Scikit-learn

BONUS SKILLS
● Management of labeling campaigns and use of the annotation tools (e.g. prodigy, dida, etc.)
● Working with geo-data and maps (POSTGIS, geopandas, QGIS, etc.)
● Any experience in NLP (using Neural Networks), data science / machine learning
● Working knowledge of AWS cloud service (EC2, S3, RDS, OpenSearch, Redshift, ECS, CloudWatch, etc.)