Senior Data Scientist
Senior Data Scientist
Tredence Inc.This job is no longer accepting applications
Job Description
We are looking for a SeniorData Scientist who is passionate about solving complex business problems using data. The ideal candidate will have strong hands-on experience in Python, SQL, and advanced machine learning algorithms, along with domain expertise in forecasting, pricing optimization, or inventory planning. You will work closely with cross-functional teams including product, engineering, and business stakeholders to drive data-driven strategies.
Key Responsibilities:
- Design, build, and deploy predictive and optimization models for use cases such as demand forecasting, dynamic pricing, or inventory optimization.
- Translate business problems into analytical frameworks and provide actionable insights.
- Build and maintain scalable data pipelines and model workflows using Python and SQL.
- Collaborate with data engineers to ensure model integration into production systems.
- Present findings and recommendations to leadership using data visualizations and storytelling.
- Stay current with the latest techniques in machine learning, statistics, and operations research.
- Lead and mentor junior data scientists and analysts when required.
Required Skills and Experience:
- 5+ years of experience in a Data Science or Analytics role with a strong business impact.
- Strong programming skills in Python (pandas, scikit-learn, statsmodels, etc.).
- Proficiency in SQL for data extraction, transformation, and manipulation.
- Deep understanding of machine learning algorithms (regression, classification, clustering, time series forecasting).
- Experience with one or more of the following domains:
- Demand Forecasting
- Price Optimization
- Inventory Optimization / Supply Chain Analytics
- Strong problem-solving and quantitative skills.
- Experience working with large datasets and distributed computing tools (e.g., Spark, Hadoop) is a plus.
- Familiarity with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).
Preferred Qualifications:
- Master's or PhD in Computer Science, Statistics, Mathematics, Economics, Operations Research, or a related field.
- Experience in retail, e-commerce, manufacturing, or logistics is a strong plus.
- Exposure to optimization libraries (e.g., PuLP, OR-Tools, Gurobi) is a bonus.
- Experience deploying models using MLOps tools or platforms is a plus.
More Info
Key Skills
scikit-learn
data visualization tools
statsmodels
About Company
Skills:
Time Series Analysis, Machine Learning, Sql, Tensorflow, Numpy, Pandas, Pytorch, Python, scikit-learn, probabilistic forecasting, Statistics, statsmodels
Skills:
Python, Machine Learning, R, ML algorithms, data science pipeline, data-structures
Skills:
model selection , Nlp, Pyspark, Neural Networks, Python, Machine Learning, Google Cloud Platform, Classification, Data Text Mining, Big Data Platforms, Finetuning, Multi-modal Models, GPU/CPU Based Online Inferencing, LLM Gateways, Deep Neural Networks, Event Driven Architecture, Vector Databases, Optimization Methods, Embedding Generation, Model Deployment, Bayesian Models, Retrieval Augmented Generation, Prompt Engineering, Deep Learning Algorithms
Skills:
causal inference , Databricks, Python, Git, double debiased ML, uncertainty quantification, heterogeneous treatment effects, scikit-learn, Experimental Design, optimization logic, price elasticity estimation
Skills:
Azure Data Factory, Azure Databricks, Statistical modeling for access risk analysis, SQL and NoSQL database management, Implementation of security and compliance controls in Azure, Microsoft Azure Active Directory administration, Microsoft Azure Privileged Identity Management PIM, Power BI or similar data visualization tools, Python or R for data analysis and automation, REST APIs and integration with external data sources, Cloud governance and policy management, Azure Sentinel or other SIEM platforms, Certification in advanced Azure security solutions, Automation tools such as Terraform or Azure ARM templates, Machine learning algorithms for security threat detection