Senior Data Scientist

Agivant Technologies India•Sherman Oaks, CA

About The Position

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.

Requirements

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

Nice To Haves

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.

Responsibilities

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).
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.
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.
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.
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.