Data Scientist – United States – Remote

As a Data Scientist at Smarsh, you’ll dive into a diverse array of unstructured communications data to tackle customer challenges through careful analysis, innovative solutions, and engaging reporting.

In this role, you’ll collaborate with Senior Data Scientists while mentoring Associate Data Scientists in extracting insights from complex datasets. You should be able to work both independently and as part of a team, demonstrating a proactive approach to managing daily data requests and analyses.

This position offers a unique chance to engage with a variety of problems and solutions related to deploying machine learning and analytics research in real-world applications. Each day will involve interactions with business leaders, machine learning researchers, data engineers, and more, giving you the opportunity to enhance your skills across the entire data science process.

Key Responsibilities

• Develop and optimize machine learning models and analytics in line with established workflows.
• Conduct data annotation and quality reviews.
• Perform exploratory data analysis and investigate model failure states.
• Draft methodology, results, and insights reports, collaborating closely with senior team members.
• Provide guidance to clients and prospects on machine learning model development and fine-tuning.
• Allocate time approximately as follows:
  • 50% on exploratory data analysis and annotation
  • 20% interacting with stakeholders to understand modeling requirements
  • 30% conducting data science experiments and model building

What You Bring

• Strong foundation in statistics (hypothesis testing, ANOVA, chi-square tests, etc.).
• Proficiency in data science principles (regression, Bayes, time series, clustering, metrics like P/R and AUROC, etc.).
• Knowledge and experience with NLP techniques, both supervised and unsupervised.
• Familiarity with deep learning techniques for NLP and an understanding of LLMs.
• Excellent verbal and written communication skills.
• A collaborative spirit with a knack for building relationships.
• A self-directed approach to learning and problem-solving.

Required Education and Experience

• Bachelor’s degree in Computer Science, Applied Mathematics, Statistics, or a related scientific field.
• 2 to 5 years of experience in data and analytics (including academic experience).
• Proficiency in Python and familiarity with SQL and NoSQL databases.
• Experience with big data frameworks like Hadoop, Spark, and Kafka is a plus.
• Familiarity with data science and machine/deep learning frameworks (e.g., scikit-learn, H2O, Keras, PyTorch, TensorFlow, Pandas, NumPy).
• Experience using Git, Linux/Unix, and IDEs.

Preferred Education and Experience

• Master’s or Ph.D. in Computer Science, Applied Mathematics, Statistics, or a relevant scientific field.
• 1+ years of experience in NLP and text analytics/classification.
• Knowledge of NLP transfer learning techniques, including word embeddings (GloVe, fastText, Word2Vec) and transformer models (BERT, SBERT, Hugging Face, GPT-x).
• Experience with NLP toolkits like NLTK and spaCy.
• Understanding of microservices architecture and continuous delivery concepts in machine learning (e.g., Helm, Docker, Kubernetes).
• Familiarity with cloud platforms (AWS, GCS, Azure).

Compensation

Salary range: $120,000 – $130,000 per year. This represents Smarsh’s good faith estimate for base compensation at the time of posting. Additional bonus programs will be discussed during the recruiting process.

About Our Culture

At Smarsh, we seek lifelong learners who are passionate about meaningful innovation, humility, and humor. Collaboration is central to our work as we partner with leading communication and cloud platforms, utilizing cutting-edge AI/ML technology to empower our customers. We are a diverse, global organization committed to creating an inclusive environment where everyone can be their authentic selves. Our leadership and culture have earned us accolades as one of the Best Places to Work. Join us to discover what your best career looks like!