Position: Sr. Data Scientist
Location: San Francisco, CA
Duration: 9-24 month contract
# of Openings: 2
Top Required Skills:
- Python coding
- Machine learning
- Basic Statistics
- (most importantly) deliver the work
- Using Python to develop machine learning models and deliver the solutions for “Client” projects
All these project already started and progress very fast. “Client” is (and will continue) delivering products to their stakeholders. And all the models will be implemented in real word
At “Client”, the Data and Analytics organization is focused on unlocking the value of “Client’s” data to support the company’s Wildfire Safety Program. As part of that focus, we focus on delivering data and AI/ML centric products to support these initiatives. Some of the products that are being delivered include Remote Inspections, AI Enabled Inspections, Vegetation Management through LiDAR capabilities, Transmission Line Asset Data Foundation, Electric Distribution Asset Data Foundation, Asset Risk Modeling, and a Cloud Native Foundational Platform.
A critical part of how we operate is to apply design thinking, work and observe the Agile development methodology, and co-location. Through these principles, we work as product teams to help deliver a valuable product to our business.
We are seeking a Senior Data Scientist for the Data & Analytics Team. The successful candidate will join a product delivery team, following best-practice agile and data science techniques to deliver impactful data products for internal “Client” partners. You will participate in the end-to-end lifecycle of model development, from initial user discovery through data engineering, feature engineering, model training and validation to model deployment as part of a business-facing product. You will actively participate and practice in an open, highly collaborative Agile environment, joining an existing, mature data science practice.
- Applies machine learning and other analytical modeling methods to develop robust and reliable analytical models, including visualizations, within “Client’s” software development environment.
- Gathers, cleans, transforms, and/or reduces data from dissimilar sources from across “Client”.
- Collaborates with team members and stakeholders to effectively manage the lifecycle of a model, retraining, replacing or sunsetting models when appropriate.
- Shares and collaborates with other “Client” data scientists.
- Delivers best-in-class software as part of a software delivery team.
- Bachelor’s Degree in Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics or job-related discipline or equivalent experience
- Job-related experience, 5 years, OR Master’s Degree and job-related experience, 3 years, OR Doctorate
- Experience with the data science lifecycle, including data engineering, feature engineering, model building and evaluation, model deployment.
- Knowledge of commonly used data science programming languages, packages, and tools, especially Python.
- Understanding of data science/machine learning models and algorithms, not limited to: deep learning (CNN, RNN etc), decision trees (e.g. xgboost, random forest), unsupervised techniques (e.g. clustering, anomaly detection), natural language processing and statistical methods.
- Experience training models in a public cloud environment, using tools such as Jupyter, AWS Sagemaker.
- Ability to synthesize complex information into clear insights and translate those insights into decisions and actions.
- Ability to clearly communicate complex technical details and insights to colleagues and stakeholders.
- Knowledge of the mathematical and statistical fields that underpin data science
- Knowledge of systems thinking and decomposition of complex problems.
- Humble – is open to being coached, has high EQ and is self-aware
- Hungry – desires to get things done while honoring people, and seeks better ways to do the job
- Collaborative – has strong interpersonal skills; cares about and works well with teammates
- Experience working on an agile delivery team, using methods such as Scrum, Kanban.