Data science+ data bricks

Senior Data Scientist

• Apply

  data science and machine learning

  techniques to

  develop and deploy

  data-driven insights.


• Build and optimize

  segmentation models, recommendation systems, forecasting solutions, churn prediction models, and product analytics frameworks

  .


• Design and develop

  predictive and prescriptive analytics models

  to support strategic decision-making.

• Partner with

  Product, Sales, Engineering, Marketing, and Finance

  to define data-driven strategies.


• Work with

  Engineering, Product Management, and Customer Success

  to analyze product usage trends and develop actionable insights.


• Lead discussions with stakeholders to

  gather evolving business requirements

  , define

  project OKRs and milestones

  , and communicate progress effectively to

  non-technical audiences

  .

• Advocate for

  data-driven decision-making

  across the organization.


• Develop

  self-service internal data products

  to empower teams with easy access to

  relevant insights

  .


• Represent the

  data science discipline

  within the company, ensuring

  best practices

  are followed.

• Mentor and

  guide junior data scientists

  , providing support in

  project planning, technical decision-making, and code/documentation review

  .


• Drive continuous improvement in

  data science methodologies, tools, and processes

  .

• 5+ years

  of experience in

  data science and machine learning

  .


• Strong expertise in

  Python, SQL, and ML frameworks

  (e.g., TensorFlow, PyTorch, Scikit-learn).


• Experience with

  Databricks, Spark, or other big data platforms

  .


• Hands-on experience with

  recommendation systems, forecasting models, segmentation, and product analytics

  .


• Strong understanding of

  A/B testing, statistical analysis, and feature engineering

  .


• Ability to

  translate business challenges into data-driven solutions

  .


• Excellent

  stakeholder management, communication, and storytelling skills

  .

• Experience in

  MLOps, model deployment, and cloud platforms (AWS, GCP, Azure)

  .


• Knowledge of

  deep learning, NLP, and time-series forecasting

  .


• Prior experience in a

  high-growth tech environment or SaaS company

  .