As a Data Scientist at Smarsh, you will be analyzing a wide range of unstructured communications data to address problems for our customers through attentive listening, astute planning, and out of the box thinking to develop solutions and reporting results in an engaging manner. The role will involve working with Senior Data Scientists and mentoring Associate Data Scientists in analyzing complex data, generating insights, and creating solutions as needed across a variety of tools and platforms. The ideal candidate for this position will possess the ability to perform both independent and team-based research and generate insights from large data sets with a hands-on/can do attitude of servicing/managing day to day data requests and analysis. The role offers a unique opportunity to get exposure to many problems and solutions associated with taking machine learning and analytics research to production. On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more, enabling you to level up in true end-to-end data science proficiency.
What will you do?
Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
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
50% exploratory data analysis and annotation
20% interaction with stakeholders to understand modeling needs
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
Required Education and Experience
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
Preferred Education and Experience
Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field
Have 1+ years experience working with NLP, text analytics/classification
Knowledge of NLP transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, HuggingFace, and GPT-x etc.)
Experience with natural language processing toolkits like NLTK, spaCy
Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
Cloud computing (AWS, GCS, Azure)
About our culture Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world’s leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.
