Our client is a rapidly growing start-up and is growing their Data Analytics team! They are looking for several Data Scientists that specialize in gathering environmental data. Your main responsibility will be to develop quantitative solutions and help support new techniques to address challenges in large datasets. You will also be translating clients' data requirements into technical development encompassing data profiling, metadata enrichment, provenance and lineage, exploration, statistical analysis, data mining, machine learning, visualization, modeling, and reporting.
- This is a 6 months contract to hire position. Only W2 or 1099 individual accept.
- 100% Remote Work.
- Analyze system requirements and design responsive algorithms and solutions.
- Selecting features, building and optimizing classifiers using machine learning techniques.
- Data modeling and ability extract insights from various data sources.
- Building insights and dashboards for our customers.
- Enhancing data collection procedures to include information that is relevant for building analytic systems.
- Interact with existing analysts and design tooling in support of their operations, including evaluating existing modeling and display in Excel for upgrade and background deeper analysis opportunities.
- 3+ years of professional work experience in Data Science or Analysis.
- 3+ years of experience with statistical tools such as SPSS, SAS, Stata, and/or other relevant predictive and modeling software.
- 3+ years of common data science toolkits such as R, Anaconda Python, Julia, and Apache MADlib.
- Data visualization tools and graphical libraries like Tableau, Business Object, Plotly, D3.js, GGplot, etc.
- Background in statistics methodology.
- Applied statistics skills, such as a complete understanding of probabilistic distributions, ability to perform parametric and non-parametric statistical testing, regression analysis, and latent variable models
- Background in computer and programing
- Understanding (assumptions and drawbacks) of statical models and machine learning algorithms, such as generalized linear models, k-NN, Naive Bayes, tree-based methods, mixture models, SVM, random forests, neural networks, etc.
- Data mining using state-of-the-art methods on large spatio-temporal datasets derived from agricultural production systems
- Experience in working with large-scale spatial and temporal data
- Experience with ArcGIS or other geographic information systems (GIS) platform would be beneficial
- Provide successful cases of data analysis such as peer review papers, github project, or any other related result published on analytical and Client content platforms
Thanks for applying!