Causal Inference And Reasoning Ml Scientist

Company Description:

We are a Silicon Valley start-up. Join our dynamic team of Data Scientists and Engineers as we perform state-of-the-art research and development in the realms of data analysis, quantitative causal inference, counterfactual reasoning, and pricing and forecasting.

Job Offer:

We offer a competitive salary, remote working options, and equity.

Essential Duties:

• Process and gain insight from large amounts of multimodal relational data (e.g. time-series, text, images, graphs, spatio-temporal processes).
• Perform research and development in the fields of quantitative causal inference and counterfactual reasoning, causal reinforcement learning, and the intersection of causal inference and machine learning/reasoning (natural language processing).
• Pricing and forecasting models

Required Skills:

• Good communication (verbal and written) skills, active participation in R&D team activities is required. Able and willing to occasionally travel.
• Experience developing innovative solutions based on the application of relevant research results from a wide variety of literature i.e. from theory to software. Experience working on projects requiring group software development and version control is a plus.
• Deep understanding of natural language processing techniques and procedures for efficiently acquiring and validating human-labeled data.
• Good understanding of quantitative disciplines such as statistics, machine learning, operations research, and causal inference.
• Experience producing and A/B testing different machine learning models Familiarity with advanced causal inferences techniques and contextual bandit algorithms preferred.
• Research experience developing and implementing causal inference and counterfactual reasoning, causal reinforcement learning, machine learning/reasoning, data mining algorithms and software tools.
• Familiarity with programming languages e.g. python and machine learning libraries e.g. SciKit Learn, Spark MLLib.

Desired Skills:

• Background in causal inference and one or more of the following areas: machine learning, machine reasoning, computer vision, network science/graph theory, text mining, statistics.
• Strong Python programming skills, in addition to R, C/C++(a plus), Java, and/or Matlab.
• Experience in using machine learning tools and packages (e.g., Caffe, PyTorch, TensorFlow, Sklearn), COTS reinforcement learning simulators (e.g. OpenAI, PettingZoo, etc.), and distributed software (e.g. Ray, Python multi-processing) is a plus.

Required Education:

Ph.D. in Computer Science, Data Science, Network Science, Applied Math, Statistics, EE or related fields.

Physical Requirements:

Good communication (verbal and written) skills, active participation in R&D team activities is required.