Data Scientist (Recent MS Grads)
Spring MS grads...today is your lucky day.
Whatever the reason, you're nearing the end of your academic career and find yourself scrambling to get your big break into industry work. Now is not the time to settle, or panic. It's simply time to create your own luck, knowing that you found this job posting for a reason. Lucid is not your average start-up. We are on a mission to inspire the adoption of sustainable energy by creating the most captivating luxury electric vehicles, centered around the human experience.
Put your education to work and take us to the next level.
Lucid's Digital team recently opened up a number of jobs tailored specifically for recent MS graduates who are looking to learn, grow and get in on the ground floor of our rapidly accelerating start-up. Working alongside some of the most accomplished minds in the industry, you will use your graduate level experience in ML, Analytics & AI to advance this decade's most exciting brand in automotive.
Bring your fresh ideas and collaboration skill. Bring your compassion for the environment. Bring your thirst for creating something that actually matters. They will all be nurtured here.
You pursued a MS degree for a reason. Let Lucid help you use it by driving to create a better, more sustainable future.
The Role
The Data Science and Machine Learning team at Lucid leads the mission to design, develop, and deploy AI solutions to help make safe and sustainable products and services on par with Lucid’s Luxury brand. Data generated by electric vehicles is rich and diverse and driving insight from them is a very exciting and challenging task. This opportunity involves defining the path to discover trends, patterns and hidden insights from data while collaborating with the most brilliant talents in the automotive industry to build AI and Machine Learning products.
- Work on state-of-the-art large-scale data science and machine learning projects
- Use mathematical techniques to arrive at an answer using data. Translate analysis results into business recommendations.
- Extract actionable insights from broad, open-ended questions.
- Partner with other data scientist and machine learning engineers to identify and articulate opportunities across the company and adapt analytics, machine learning and data mining algorithms to solve problems across several engineering and business domains.
- Use quantitative analysis and the presentation of data to see beyond the numbers and understand what can improve our processes.
- Engage broadly with the organization to identify, prioritize, frame, and structure complex and ambiguous engineering challenges, where advanced analytics, AI and machine learning can have the biggest impact.
Office location at Newark, California, or Beaverton, Oregon.
Qualifications
- MS degree (Spring 2022) in Computer Science, Computer Engineering, Electrical Engineering, Mechanical engineering, Data Analytics, Statistics, or related STEM field.
- Strong programming skills in Python, and SQL.
- Technical mastery and in-depth knowledge in one or more of the following topics: Statistics, Anomaly detection and signal processing, time-series data analysis and modeling, Conventional Machine Learning methods, Statistical Learning, Computer Vision, Deep learning, CNNs, Natural Language Processing (NLP), text mining, sentiment analysis, etc.
- Experience with deep learning frameworks such as TensorFlow, Keras, Pytorch, Caffe, MXNet, etc.
- Relevant knowledge of applying advanced analytics, statistics, and data mining techniques.
- Familiarity with data presentation and visualization with tools such as Tableau, Grafana, PowerBI, and other similar tools.
- Excellent communication and presentation skills.
Nice to Have
- Relevant experience in EV or Automotive, Transportation and autonomous driving is a plus
- Experience with telematics data analytics and decision modeling is a plus
- Experience with big data platforms such as Spark/Hadoop, Presto, and Hive is a huge plus
- Hands-on experience with cloud computing and HPC or other cloud and distributed platforms e.g. Airflow, Kubernetes, Kubeflow, MLFlow, etc. is a huge plus