Associate Director / Principal Computational Statistician

The AD/Principal Computational Statistician will partner with the VP of the AI Lab.

This role is a hybrid of a manager role and individual contributor. The person in this role will partner with the V.P. of the A.I. Laboratory to oversee its daily operations and will help pioneer the use of machine-learning/deep-learning in genomics. The person in this role will assist in the development of statistical-learning approaches to analyze various large-scale genomics datasets to better understand disease etiology, identify novel drug targets, and discover biomarkers for use in precision medicine. This position will report directly to the Vice President of Statistical Sciences.

Essential functions

• Partnering with Vice President/Head of the A.I. lab to coordinate and overseeing daily operations, which includes managing staffing responsibilities (hiring, training, performance reviews, etc.).
• Assisting in overseeing the development and application of advanced, cutting-edge computational statistics and bioinformatics methodologies.
• Utilizing machine-learning and statistical approaches to analyze large sets of genomic data.
• Partnering with coworkers on various client projects.
• Participating in project work related to writing publications of research findings in top-tier scientific journals Assist in developing and implementing research and commercial objectives for the Statistical Sciences Department
• Ensuring compliance with company regulations and internal policies.
• Other duties as assigned by V.P.

 Required education and experience

• Ph.D. in Engineering, Computational Statistics, Computer Science, or related field with a minimum of 5 years of industry or academic experience
• At least 5-8 years’ management experience, leading teams
• Proven knowledge of statistics (Bayesian and frequentist inference), machine learning, and data analysis
• Advanced specialization in mathematical modeling, machine learning, deep learning, statistical analyses, predictive modeling and algorithm development
• Advanced programming skills with fluency in at least Python or R, with extensive experience using modern machine learning and deep learning libraries (TensorFlow, PyTorch, Edward, sklearn, caret, etc.)
• Proven ability to design and code production grade machine learning and deep learning applications, along with a strong ability to visualize ‘big-data’
• Ability to manage cloud computing environments (e.g. AWS) with experience working with GPUs
• Strong communication and presentation skills with the ability to translate and communicate complex results to individuals of diverse backgrounds
• Team oriented and highly collaborative with strong organizational and communication skills

Preferred education and experience

• Ph.D. in Engineering, Computational Statistics, Computer Science, or another related field with 5 years of experience in performance and operations management in industry or academia
• Proven ability to manage multiple high-profile genomics projects simultaneously
• In-depth understanding of modern genomics analysis including RNA-seq, single-cell RNA-seq, DNA methylation, variant analysis, etc.
• Working knowledge of biology (oncology, immunology, autoimmunity, etc.) and target identification
• Knowledge of publicly available genomics databases (i.e. ENCODE, GEO, TCGA, CCLA)
• Up-to-date knowledge of the fast-moving machine-learning/deep-learning literature

Apply

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