The Role
Develop and implement time series forecasting models using statistical, machine learning, and deep learning methods. Clean and process large datasets, validate data, detect anomalies, collaborate with stakeholders, communicate insights, and optimize models for business use in supply chain, demand planning, and inventory.
Summary Generated by Built In
Data Scientist
Primary Skills
- Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools(KubeFlow, BentoML), T-Test, Z-Test
Specialization
- Data Science Advanced: Data Specialist
Job requirements
- Key Responsibilities: Develop and implement time series forecasting models to predict future trends, demand, and performance metrics. Leverage statistical methods, machine learning, and deep learning techniques for accurate forecasting. Work with large datasets from multiple sources and clean, process, and transform data for modeling purposes. Analyze historical data patterns and trends to identify key drivers of future outcomes. Perform data validation, anomaly detection, and ensure data integrity. Collaborate with business and technical teams to understand forecasting needs and ensure models align with business objectives. Communicate findings, insights, and predictions to both technical and non-technical stakeholders. Continuously optimize and improve forecasting models by experimenting with new algorithms, tools, and techniques. Use forecasting models to drive business decisions related to supply chain, demand planning, inventory, and more. Document processes, models, and code to ensure scalability and reproducibility. Key Requirements: Education: Bachelor's or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related field. Experience: 5-6 years of experience in data science with a focus on time series forecasting. Proficiency in Python or R for data analysis and modeling. Strong knowledge of time series forecasting techniques such as ARIMA, SARIMA, ETS, Prophet, or LSTM. Experience with machine learning algorithms and statistical analysis. Familiarity with tools like SQL, Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Experience working with large datasets and data pipelines in a cloud-based environment (AWS, Azure, or GCP). Strong problem-solving skills and the ability to interpret complex data. Excellent communication and presentation skills to translate complex data findings into actionable insights for business teams.
Skills Required
- Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field
- 5-6 years of experience in data science with a focus on time series forecasting
- Proficiency in Python or R for data analysis and modeling
- Strong knowledge of time series forecasting techniques (ARIMA, SARIMA, ETS, Prophet, LSTM)
- Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, CNTK, Keras, MXNet)
- Familiarity with SQL, Pandas, NumPy and working with large datasets/data pipelines
- Experience with cloud-based environments (AWS, Azure, or GCP)
- Experience with data quality and monitoring tools (Great Expectations, Evidently AI)
- Experience with model deployment/ML tooling (KubeFlow, BentoML)
- Knowledge of statistical testing (T-Test, Z-Test), regression methods (linear, logistic), and SAS/SPSS
- Strong problem-solving, communication and presentation skills to translate findings to business stakeholders
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
