Senior Data Scientist

Role
Overview

We are looking for a Mid‑Level Data
Scientist who is passionate about turning data into actionable business
insights. This role sits at the intersection of data analysis, statistical
modeling, and business decision‑making. You will work closely with
stakeholders across Product, Marketing, Engineering, and Leadership to design
models, monitor performance, and influence strategy through data.

The ideal candidate can independently own data
problems end‑to‑end—from understanding the business context, exploring data,
building models, and communicating insights clearly.

Key
Responsibilities

Analytics
& Modeling

• Analyze large, structured time‑series datasets to uncover trends,
  correlations, and causal signals.
  
• Build and evaluate statistical and machine learning models such as:
  
• Regression models (linear, regularized)
  
• Time‑series and seasonality models
  
• Classification and anomaly detection models
  
• Apply concepts of incrementality, attribution, and ROI analysis to quantify impact.
  
• Design experiments or quasi‑experiments (A/B testing, pre‑post
  analysis, causal inference).
  

Business
Problem Solving

• Translate ambiguous business questions into well‑defined analytical
  problems.
  
• Partner with stakeholders to identify key metrics, assumptions, and
  success criteria.
  
• Distinguish between correlation and causation when presenting
  insights.
  
• Provide clear, data‑backed recommendations that influence
  decisions.
  

Monitoring
& Automation

• Develop automated monitoring systems for core KPIs (e.g., revenue,
  spend, conversions).
  
• Identify and flag statistically significant deviations while
  minimizing false positives.
  
• Incorporate seasonality, trends, and known events into analytical
  logic.
  
• Support root‑cause analysis when anomalies or performance drops
  occur.
  

Data
Engineering & Tooling

• Write efficient, production‑ready SQL and Python code.
  
• Work with data pipelines, data warehouses, and dashboards.
  
• Ensure data quality through validation, sanity checks, and
  documentation.
  
• Collaborate with Data Engineers to improve data availability and
  reliability.
  

Communication
& Collaboration

• Communicate findings clearly to both technical and non‑technical
  audiences.
  
• Create concise presentations, dashboards, and written summaries.
  
• Review peers’ analyses and models; contribute to best practices
  within the team. 
  
• Mentor
  junior analysts or data scientists where needed
  

Requirements

Required
Skills & Qualifications

Technical
Skills

• Strong proficiency in Python (pandas, numpy, scikit‑learn,
  statsmodels).
  
• Solid SQL skills for querying and transforming large
  datasets.
  
• Good understanding of statistics:
  
• Hypothesis testing
  
• Confidence intervals
  
• Regression analysis
  
• Bias, variance, and assumptions
  
• Experience working with time‑series data and seasonality.
  
• Familiarity with data visualization tools (e.g., matplotlib,
  seaborn, Tableau, Looker, Power BI).
  

Conceptual
Understanding

• Clear understanding of:
  
• Correlation vs causation
  
• Incrementality and attribution concepts
  
• Model evaluation and validation
  
• Ability to reason through tradeoffs in modeling choices.
  

Soft Skills

• Strong problem‑solving and critical‑thinking abilities.
  
• Comfortable working with ambiguity and incomplete data.
  
• Clear written and verbal communication skills.
  
• Curiosity,
  ownership mindset, and bias toward action
  

Good to
Have

• Experience with marketing, e‑commerce, fintech, or growth
  analytics.
  
• Exposure to:
  
• Media Mix Models (MMM)
  
• Bayesian modeling
  
• Anomaly detection techniques
  
• Experience with cloud data platforms (BigQuery, Redshift,
  Snowflake).
  
• Familiarity with workflow orchestration or production monitoring.