Data Scientist - Data Infrastructure & Tools

Description:
Client seeking a Data Scientist with a focus on scaling infrastructure to support data science work. This includes labeling & visualization systems/platforms, and data processing and transformation. 
In this role, you will join a dynamic team utilizing petabyte-scale datasets for advanced analytics and model building to develop data tools and infrastructure around labeling & system performance management systems. 
Your expertise will contribute to enabling intelligent, automated equipment and improved decisions. 
We are looking for individuals who are passionate about applying innovative technology to solve some of the world's most substantial problems.

Key Responsibilities:
Design, build, and scale tools and infrastructure around data labeling systems (e.g., Super Annotate, Label box, Voxel51), including the development of plugins for spatial visualizations over maps.
Scoping out work and estimating timelines, work in an Agile environment, and executing on deliverables.
Define, quantify, and analyze key performance Indicators to support successful outcomes.
Collaborate with other data scientists and engineers to build and test solutions.
Communicate your progress, recommendations, methodologies, and insights with stakeholders from various backgrounds.

Required Skills:
Demonstrated competency in developing production-ready code using Python & AWS infrastructure (e.g., S3, policies, etc.).
Proficiency in using data-access technologies such as SQL, Spark, Databricks, MongoDB.
Experience with data storage and management of various types of data (e.g., images, raster’s, parquet, time-series, geo-tagged, text, and other unstructured data),
Familiarity with spatial visualization tools (e.g., Folium, Plotly), and labeling systems like Super Annotate, Label box, Voxel51.
Experience in developing alerts (e.g., through email automation), dashboards (e.g., Tableau), or other means to monitor system performance and issues.
Excellent communication skills, including the ability to lead meetings, document work for reproduction, write persuasively, communicate proof-of-concepts, and effectively take notes.
Capacity to communicate complex analytical insights in a manner understandable by non-technical audiences.

Work Experience: 
(1 - 3 years) : Data analytics experience. 
(1 - 3 years) : Background or proven experience in mining data for analytics insights.
(1 - 3 years) : Good exposure to enterprise statistical tools like SAS, Statistics, SPSS, or SAS E Miner

Preferred Skills:
Experience developing plugins with other programming languages such JavaScript.
Knowledge of model validation, measuring model bias, and measuring model drift.

Education: 
4 years bachelor’s degree