Data Scientist

Insight Global; Sunnyvale (CA); On-site; USD 143,000 - 286,000; Full time

Benefits offered by this job

Vision insurance

Medical insurance

401(k)

Paid maternity leave

Paid paternity leave

Job summary

A leading analytics firm is seeking a mid-senior level Data Scientist in Sunnyvale, CA. The role involves leading the design and deployment of large-scale time series forecasting models and continuously enhancing forecasting strategies using advanced machine learning methods. Key responsibilities include mentoring junior scientists and building experimentation pipelines. Proficiency in Python, SQL, and causal inference techniques is essential. This full-time position offers competitive benefits, including medical insurance and 401(k) contributions.

Qualifications

• Strong foundation in Causal Inference and Statistical Analysis.
• Hands-on experience with various ML techniques and their applications.
• Ability to integrate statistical expertise with machine learning methods.

Responsibilities

• Lead design and deployment of large-scale forecasting models.
• Enhance strategies using advanced machine learning architectures.
• Build and maintain experimentation pipelines for causal impacts.
• Mentor junior scientists and ensure code reproducibility.

Skills

Causal Inference

Statistical Analysis

Machine Learning methods

Python

SQL

PyTorch

Spark/Ray

Job description

Base pay range

$143.00/yr - $286.00/yr

Job Description:

Insight Global's client is hiring for Staff/Senior/Principal level Data Scientists to join their team focused in either time series forecasting or GenAi. Their team collaborates closely with Finance teams to enhance financial planning and strategic decision‑making through cutting‑edge data‑driven solutions. They specialize in a range of initiatives that provide actionable insights into trends and patterns and leverage Generative AI (Genai) to produce concise, insightful summaries that empower decision‑makers. By integrating these innovative approaches, we strive to drive efficiency, accuracy, and impactful outcomes in financial operations.

Preferred Requirnments:

• Strong foundation in Causal Inference, Statistical Analysis, and advanced Machine Learning methods
• Hands‑on experience with a wide range of ML techniques, with a deep understanding of their advantages and limitations across different scenarios.
• Ability to integrate statistical expertise with machine‑learning methods to maximize the value and interpretability of ML solutions.
• Proficiency in Python, SQL, PyTorch, Spark/Ray, and stats/econometrics libraries.
• Experience deploying ML systems at scale on cloud platforms (GCP/Azure).

Key Responsibilities:

• Lead the design, development, testing, and global deployment of large‑scale time series forecasting models (including Regression models and state of the art time series specific models for example N‑BEAST, PatchTST) to support complex retail and e‑commerce hierarchies. Introduce causal modeling approaches to conduct impact analysis for future forecast.
• Continuously enhance forecasting strategies by incorporating advanced machine learning architectures, including RNNs (sequence modeling), CNNs (temporal feature extraction), and Attention‑based mechanisms to improve accuracy, scalability, and robustness in time series forecasting.
• Advance causal modeling frameworks to quantify event impacts and integrate causal insights into forward‑looking forecasts.
• Build and maintain experimentation pipelines (A/B testing, quasi‑experiments, multi‑armed bandits) for evaluating causal impacts of interventions.
• Mentor junior scientists, review research and production code, and ensure reproducibility and scalability in pipelines.
• Collaborate with engineering to implement forecasting + optimization systems in production (Airflow, Astronomer, Spark/Ray).
• Act as technical lead on multiple projects, balancing research rigor with business delivery.

Seniority level

Mid‑Senior level

Employment type

Full‑time

Job function

Engineering and Information Technology

Industries

Retail

Vision insurance

Medical insurance

401(k)

Paid maternity leave

Paid paternity leave