Smart Device Services Data Scientist
- Identifies research, tools, and analyses required to achieve objectives for large, complex machine learning problems
- Structures analytical approaches and develops project strategies and tactics
- Consults with senior management on a wide range of issues related to smart device capabilities, machine learning opportunities
- Strong, proven background in developing real world machine learning algorithms including, conducting exploratory data analysis, cleaning/imputing noisy/missing data, feature engineering, model building and validation.
- Good understanding of probabilistic methods for statistical inference and decision making including Bayesian and Frequentist statistics.
- Understanding of the theory and practical usage of machine learning methods like Naive Bayes, Logistic regression, SVM, Kernel methods, decision trees, random forests, CART models, Graphical models (Bayesian an Markov Networks), Latent linear models (ICA,PCA, LDA, matrix factorizations) etc. Familiarity with emerging state of the art methods like deep learninga plus.
- Participation and strong performance in relevant data science competitions/competitive courses a strong plus.
- Strong implementation skills in deploying and scaling cloud based machine learning models on big data
- Working knowledge of Amazon AWS ecosystem, especially for deploying big data system architectures, including serverless event driven architectures (AWS-lambda)
- Good knowledge of cluster compute frameworks such as Spark and Hadoop ecosystem (MapReduce, HDFS etc.)
- Good with SQL and NoSQL Databases
- Ability to anticipate opportunities and proactively pursue them
- Tenacity, confidence to tackle tough challenges head-on but with the humility required to team with others
- Understand, own, drive and deliver your own business while recognizing your role in the larger business
- Exercise independent judgment and take initiative
- Flexibility and willingness to take appropriate risk
- Promote and demonstrate a positive, fun, can-do approach to your job
- Commitment to quality & innovation
- Excellent written, verbal and interpersonal communication skills
Desired Education, Experience, Skill Sets
- Masters/PhD in Computer Science, Statistics or other quantitative field with proven track record (typically 5 years after masters or 3 years after PhD) in a data science role.
- Programming Languages: Python, R, Matlab, Scala, Java.
- Cluster compute frameworks: Spark, Hadoop
- Databases: SQL databases, dynamoDB, Cassandra
- Compute architecture: Amazon AWS ecosystem
Meet Some of HP's Employees
Elizabeth focuses on the HP consumer experience, scaling best practices across premium consumer notebooks, testing products before public release, and crafting the compelling story of their design and performance for press workshops.
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