Sr Data Scientist

The role

We are looking for senior-level Data Scientists to join our Data Engineering, and AI, team. Are you passionate about data and thrive at applying data science to solve impactful business problems? As a data scientist, you will have ample opportunities to apply your data science skillset to unlock vast business opportunities by extracting key insights using a wide range of data sources, advanced statistical models, machine learning algorithms, and modern AI techniques, and translating solutions into meaningful, goal-oriented business actions. Each day, you will be presented with a variety of new challenges and interesting projects that tap your interests and strengths.

Responsibilities:

• Collaborate with both our team and cross-functional partners in manufacturing, operations, finance, marketing, and engineering to understand business need and scope data science projects.
• Wrangle, process, cleanse, verify, and enrich data from different sources used for analysis.
• Help build data-informed business strategy and roadmaps.
• Translate business needs into data product requirements, evaluate technologies, and identify opportunities to innovate and improve our data science capabilities.
• Use creative problem-solving skills to analyze data and build statistical / machine learning models to help solve business problems from different perspectives.
• Work closely with data engineers to create services that can ingest from both internal and external sources and ensure data quality and timeliness.
• Take ownership of the entire, end-to-end data product development and deployment process of the specific product assigned to you.
• Lead the development of data visualization and dashboards to help the organization monitor performance, generate insights, and continuously improve user experience.
• Establish playbooks to drive process and consistent outcomes.
• Participating in the full life cycle of model development, which spans from business problem discovery, data discovery, data analyses, model development and testing to model deployment and monitoring.

Qualifications:

• Master’s or Doctoral degree in Computer Science (with a focus on Data Mining, Machine Learning), Statistics, Econometrics, Operational Research, or other rigorous quantitative disciplines that require processing and modeling data at a complex and large scale.
• 4+ years of professional experience as a professional data scientist with extensive experience in statistical and machine learning modeling.
• Proficient in Python, SQL, Spark, the associated Python and Spark packages commonly used by data scientists, and Deep Learning libraries, such as PyTorch.
• Proficient in wrangle and analyze data with complex relationships and time scale.
• Strong understanding of and practical experience in a wide range of machine learning algorithms, statistical modeling, and modern AI techniques.
• Experience in using data visualization and dashboard tools.
• Working experience in cloud data technology.
• Experience working with large structured and unstructured datasets.
• Out-of-the-box thinker and problem solver who can turn ambiguous business problems into clear data-and-ML-driven solutions that deliver impactful business results.
• Excellent organizational skills, verbal and written communication skills, and presentation skills.
• The following is a plus:
  • Basic knowledge of data engineering and MLOps.
  • Proficient in other languages, such as R, used in data science.
  • Certificates earned in data mining, machine learning, deep learning, statistical modeling, cloud computing, and data engineering.

Locations

• Los Angeles

In Los Angeles, the salary range for this position is $152,000-$190,000, which represents base pay only and does not include short-term or long-term incentive compensation. In New York City, the salary range is $168,000-$210,000. These salary ranges are specific to Los Angeles or NYC and may not be applicable to other locations. When determining base pay, as part of a final compensation package, we consider several factors such as location, experience, qualifications, and training.