Description

We are seeking an experienced Senior Machine Learning Engineer/Data Scientist with a strong focus on predictive modeling and time series forecasting. In this role, you will lead the development of advanced forecasting models and predictive analytics solutions to support key business objectives. You will also leverage domain-specific knowledge in Business Analysis, Financial Analysis, and Marketing Analytics to drive insights and strategies that align with organizational goals. Additionally, you will explore and contribute to projects involving Generative AI as a secondary area of expertise. This role requires deep expertise in statistical methods, trend identification, data-driven decision-making, and strong collaboration and stakeholder communication skills.

Primary Responsibilities/Essential Functions

1. Predictive Modeling:

• Lead the development and deployment of predictive models to solve business challenges such as sales forecasting, risk analysis, portfolio management, and customer behavior prediction.

• Use advanced machine learning and statistical techniques such as regression analysis, decision trees, random forests, gradient boosting, and deep learning to build highly accurate models.

• Collaborate with stakeholders to align predictive models with business needs, ensuring they provide actionable insights for key decision-making.

2. Time Series Forecasting:

• Design and implement advanced time series forecasting models to support various business domains such as demand forecasting, revenue forecasting, and economic modeling.

• Apply techniques like ARIMA, SARIMA, exponential smoothing, Prophet, and other machine learning-based time series models to capture trends, seasonality, and autocorrelations.

• Integrate real-time data and external factors to improve the accuracy and relevance of forecasting models.

3. Statistical Analysis:

• Conduct risk analysis, economic modeling, and financial forecasting to support financial planning, portfolio management, and risk mitigation strategies.

• Utilize statistical modeling techniques, including hypothesis testing, confidence intervals, and Bayesian analysis, to enhance the reliability and robustness of predictions.

• Collaborate with business analysts and finance teams to translate complex statistical findings into actionable business recommendations.

4. Exploratory Data Analysis (EDA):

• Lead Exploratory Data Analysis (EDA) using descriptive statistics and visualization tools to uncover trends, patterns, and outliers.

• Use tools like Matplotlib, Seaborn, and other visualization libraries to present data insights effectively to stakeholders, ensuring clarity and actionability.

5. Generative AI (Secondary Skill):

• Contribute to Generative AI initiatives, such as creating synthetic data for model training, generating simulations, or enhancing existing models with generative techniques.

• Explore innovative use cases for Generative AI within the business, such as campaign analysis and marketing content generation.

• Stay informed about advancements in Generative AI and assess their applicability to current business needs.

Domain-Specific Knowledge a plus:

Business Analysis:

• Collaborate with stakeholders to understand business requirements, translating them into technical solutions that align with company objectives.

• Lead stakeholder communication, ensuring that complex technical insights are presented in a clear and actionable manner for decision-making.

Financial Analysis:

• Conduct risk analysis and develop models for portfolio management and economic modeling.

• Use data-driven insights to support financial decisions and help optimize investment strategies, ensuring that risk is properly managed, and opportunities are capitalized.

Marketing Analytics:

• Apply machine learning techniques for customer segmentation, improving marketing effectiveness and targeting.

• Lead campaign analysis and A/B testing to determine the effectiveness of marketing strategies and optimize future campaigns.

• Work closely with marketing teams to leverage predictive insights for campaign success and customer engagement.

Qualifications

• 5 years of experience in machine learning, predictive modeling, and time series forecasting, with a focus on business applications.

• Proven track record of leveraging domain-specific knowledge in Business Analysis, Financial Analysis, and Marketing Analytics to drive business results.

• Experience or exposure to Generative AI techniques such as GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders) is a plus.

Technical Skills:

• Proficiency in Python and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, and statsmodels.

• Expertise in time series forecasting models (e.g., ARIMA, SARIMA, Exponential Smoothing, Prophet) and predictive modeling.

• Strong understanding of statistical analysis, including hypothesis testing, regression analysis, and confidence intervals.

• Experience with data visualization tools like Matplotlib, Seaborn, and business intelligence tools.

• Knowledge of financial modeling, risk analysis, and portfolio management techniques is a plus.

Leadership and Communication:

• Strong communication skills, with the ability to present complex data insights clearly to technical and non-technical stakeholders.

• Act as a key influencer in business decision-making, using machine learning to drive strategies in finance, marketing, and operations.