Job Description

Key Responsibilities

Apply

statistical analysis, machine learning, and predictive modeling

to develop business-driven solutions.Build and optimize models for

classification, regression, and causal inference

.Work with supervised learning methods including

XGBoost, Ridge, ElasticNet, and Neural Networks

.Design and implement

predictive analytics models

for real-world use cases.Apply

causal inference and optimization techniques

to derive actionable insights.Collaborate with cross-functional teams (data engineers, business analysts, stakeholders) to integrate models into production systems.Perform

model validation, performance evaluation, and tuning

to ensure scalability and accuracy.Communicate findings and insights effectively to both technical and non-technical stakeholders.

Skills & Expertise Required

Strong knowledge in

Machine Learning & Predictive Analytics

.Hands-on experience with models:

XGBoost, Ridge Regression, ElasticNet, Neural Networks

.Expertise in

causal inference and optimization methodologies

.Proficiency in

Python/R

for statistical modeling and data manipulation.Experience with

SQL

and working with large datasets.Solid understanding of

statistical concepts, data visualization, and feature engineering

.

Preferred Skills

Exposure to

cloud-based ML platforms (AWS, Azure, GCP)

.Knowledge of

MLOps and deployment of models into production

.Familiarity with

Big Data tools (Spark, Hadoop)

.

Job Type: Full-time

Pay: ?1,300,000.00 - ?1,600,000.00 per year

Work Location: In person