Data Scientist - #941454

DocuSign


Date: 1 week ago
City: Bengaluru, Karnataka
Contract type: Full time

  • The Global Data Analytics (GDA) Data Scientist is a highly motivated self-starter who is responsible for designing, building, and promoting models and algorithms that power the next generation of machine learning and data science products for the various organizations at DocuSign
  • You will be solving difficult and non-routine problems by applying analytical methods in novel ways
  • This includes processing, analyzing and interpreting large and complex data sets, with an emphasis on actionable results
  • You will also need to collaborate closely with Sales (GTM), Customer Success, Product, Engineering and other stakeholders to implement model-based solutions, measure the effectiveness of data products and drive growth and customer success
  • This role will influence and shape the design, architecture, and roadmap for predictive and prescriptive data products



Responsibility

  • Collaborate with a cross-functional agile team spanning data science, data engineering, product management, and business experts to build new data products that advance our mission to understand our platform and help us sustainably grow as a business
  • Execute Data Science projects end-to-end, ensuring cross team collaboration and partnership with business
  • Contribute to designing, building, evaluating, shipping, and refining our data products by hands-on ML development
  • Build and drive product recommendation systems that support the DocuSign Agreement Cloud
  • Support data ingestion from multiple infrastructures
  • Coordinate effective, quantitative strategies directly derived from communication with stakeholders
  • Help drive optimization, testing, and tooling to improve quality
  • Design experiments that evaluate the effectiveness of data products
  • Develop data preparation processes to consolidate heterogeneous datasets and work around data quality issues
  • Communicate and present strategic insights to non-technical audiences
  • Work within the Machine Learning platform team to deploy models to production using existing and emerging machine learning methods and technologies
  • Work with stakeholders to translate product requirements into robust, customer-agnostic machine learning success metrics


What you bring

Basic

  • Bachelor, Master s or PhD degree in Computer Science or natural sciences or other related field
  • 5+ years hands on experience in building data science applications and machine learning pipelines
  • Experience with Python and SQL both for research and software development purposes
  • Experience across the SAAS domain as a Data Scientist

Preferred

  • Knowledge of common machine learning and statistics frameworks and concepts
  • Experience with large data sets, distributed computing and cloud computing platforms
  • Proficiency with relational databases (e.g., SQL)
  • Ability to break down technical concepts into simple terms to present to diverse, technical, and non-technical audiences
  • Experience in training and deploying machine learning models in production environments
  • Knowledge of Apache Airflow, Spark, Snowflake
  • Experience working with technologies like AWS, Git and Terraform
    MLOps experience
  • Effective written and verbal communication skills
  • Experience creatively working with challenging data and systems
  • Ability to deliver in a complex and fast-moving organization at a global scale
  • Deeply analytical with a keen understanding of business processes and programs and the ability to translate data and insights into operational readouts
  • Experience using machine learning and deep learning algorithms like CatBoost, XGBoost, LGBM, Feed Forward Networks for classification, regression, clustering problems
  • Programming Languages like Python, SQL, R etc
  • MapReduce Frameworks like Spark, Hadoop etc
  • Databases like Snowflake, MySQL, Postgress
  • AWS services like MWAA, Lambda, Athena, S3, Sagemaker Experiments,
  • Model Registry etc for model training, model deploying and monitoring

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