Artificial Intelligence/Machine Learning Scientist

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

The Software Defined Vehicle team supports the definition, design, and development of continuously improving mobility services. SDV is new vehicle intelligence platform that will underpin all our future innovations across a wide range of technological advancements, including EVs and expanded automated driving. This means that you can potentially put a fingerprint on the vehicles that drive down the road! This space is ripe with technical leadership opportunities for sophisticated technology development, as well as the ability to provide mentorship for new employees. You can flex your collaboration muscles as you will be tasked with working with several other areas of engineering, IT, and the business.

As a Data Scientist, you will be applying Data Science and Modeling techniques to tackle complex business problems. Are you driven and passionate about data analytics and data sciences? Can you lead with our vision for the future? Come be part of an industry whose vision is to change the future of mobility.

This is a unique opportunity to translate expertise and experience into practices around virtuous cycles for machine learning data within a multi-disciplinary team of experienced people. We lead the design, development and deployment of advanced statistical and mathematical solutions with the help of machine learning techniques and traditional methods. You'll be part of a team that generates new algorithmic models and software, enabling companies to develop automated and autonomous driving systems.

You will support iterative development and learning cycles in the areas listed below, and ultimately produce new and creative solutions that will become part of the operating fabric of the global enterprise. The scope of the work may vary and the candidate should demonstrate proficiency in one or more of the following:

• Geospatial analysis and mapping
• Mobility and behavioral analysis
• Risk analysis and forecasting
• Comparative frameworks between sparse real-world observations, dense simulated observations, and opportunities to expand sensor or sensing frameworks
• Emerging techniques such as generative adversarial networks, self-supervised reinforcement learning, and imitation learning for complex optimization problems
• Numerical optimization, statistical modeling and forecasting, machine learning and image processing

Responsibilities:

• Provide technical guidance and subject matter expertise to team members and stakeholders
• Build predictive models and machine-learning algorithms
• Analyze large amounts of information to discover trends and patterns
• Undertake preprocessing of structured and unstructured data
• Monitor and sustain model effectiveness
• Combine models through ensemble modeling
• Present complex information using data visualization techniques
• Propose solutions and strategies to business challenges that drive business impact

The successful candidate should have experience (3+ years outside of a graduate school assistantship) across a broad set of potential roles, including:

• Research, Data, or AI/ML Scientist
• AI Analyst
• Engineer
• AI/ML Developer or Architect

Additional Description

? Qualifications:

• Candidates should have deep familiarity with predictive modeling techniques based off time series analysis with very high dimensional data sets, especially regarding kinematics
• Experience in generating geospatial feature detection models such as land use, land type, or object detection leveraging sensor fusion or imagery classification is highly desired
• Experience establishing sampling policies for understanding real-world dynamic events from small samples of data, and methodological bases for calculating comprehensive statistics needed to validate expected performance of models or generalization from small samples
• Experience translating real-world sensor data into simulated environments and vice-versa
• Familiarity with linear discriminant analysis, cluster analysis, and other methods to identify latent class features in both labeled and label-free data

Skills:

Candidates must be collaborative team players who will work closely with fellow Engineering professionals (scientists, engineers, software developers, and team leads). Further, candidates should be able to demonstrate the following capabilities:

• Working on small, high output teams in a fast-paced environment
• Excellent verbal and written communication skills with the ability to interact effectively with multiple stakeholders
• Strong problem-solving skills and analytical thinking
• Excellent attention to detail
• Ability to deep dive into data or related analyses/reports and derive useful insights to support decision making

Requirements:

• MS Degree (PhD Preferred) in Engineering, Computer Science, Physics, Mathematics, or related quantitative field
• 5 or more years proficiency in one or more core analytical tools / suites / languages such as Python, R, Spark Scala, PyTorch, TensorFlow, and understand their limitations
• Preferred experience with autonomous vehicle and/or ADAS technologies/systems and related vehicle data processing and analytics