Remote Data Scientist
Smarsh
💵 $120k-$130k
📍Remote - United States
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Job highlights
Summary
Join Smarsh as a Data Scientist and analyze unstructured communications data to address problems for customers through attentive listening, astute planning, and out-of-the-box thinking. The role involves working with Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions across various tools and platforms.
Requirements
- Solid statistics knowledge (hypothesis testing, ANOVA, chi-square tests, etc…)
- Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc…)
- Understanding and Experience of NLP Techniques in traditional pipelines, metrics, process and evaluation for supervised and unsupervised learning
- Familiarity with Deep Learning techniques for NLP
- Familiarity with LLMs
- Excellent verbal and written skills
- Proven collaborator, thriving on teamwork
- Self-learner
- Good relationship building skills
- Bachelor’s degree in Computer Science, Applied Math, Statistics, or a scientific field
- Approx. 2 to 5 years experience working with data & analytics (including school)
- Experience working with Python
- Familiarity with SQL and noSQL databases
- Knowledge of “Big Data” frameworks like Hadoop, spark and Kafka are a plus
- Familiarity with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse
- Experience using Git, Linux/Unix, an IDEs
Responsibilities
- Development of machine learning models and other analytics following established workflows
- Data annotation and quality review
- Exploratory data analysis and model fail state analysis
- Methodology, results and insights reporting, including model Governance report drafting in collaboration with senior team members
- Client/prospect guidance in machine learning model and analytic fine-tuning/development processes
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