2 Allstate Software Engineering Consultant Jobs

Key Responsibilities:

• Develop and implement advanced statistical and machine learning models to address business challenges and improve decision accuracy.

• Communicate findings and model insights effectively to stakeholders, ensuring alignment with business processes and objectives.

• Innovate and recommend new modeling techniques, tools, and approaches to enhance project outcomes.

• Collaborate with business teams to design and execute machine learning solutions that drive business value and improve overall decision-making.

• Identify opportunities for integrating new data sources, both internal and external, to enhance model performance and business outcomes.

• Develop and manage complex analytical projects, ensuring timely delivery while maintaining high-quality standards.

• Mentor junior data scientists, fostering a collaborative and growth-oriented team environment.

• Guide the strategy for machine learning and predictive modeling within the department, ensuring continuous improvement in data-driven decision-making.

Qualifications:

• Bachelors degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Engineering) or equivalent experience.

• 4-6 years of experience in data science, analytics, or quantitative modeling.

• Expertise in predictive modeling, machine learning, and statistical techniques such as clustering, classification, and regression.

• Proficiency in Python and SQL, with a strong understanding of data manipulation and analysis tools.

• Familiarity with a range of machine learning libraries and frameworks such as NumPy, Pandas, Scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, and others listed below.

• Experience with big data tools and techniques (e.g., PySpark, Dask, Vaex, Apache Airflow) is a plus.

• Strong project management skills and ability to break down complex tasks into manageable steps, ensuring timely project delivery.

• Proven ability to work collaboratively and influence business partners at all levels.

Key Tools and Libraries:

• NumPy, Pandas, SciPy: Scientific computing, data analysis, and modeling.

• scikit-learn, XGBoost, LightGBM, CatBoost: Predictive modeling, classification, regression, and large datasets.

• TensorFlow, Keras, PyTorch: Deep learning, neural networks, distributed training.

• Plotly, Matplotlib, Seaborn, Altair: Data visualization, interactive dashboards, statistical plots.

• PySpark, Dask, Vaex: Big data processing and real-time data handling.

• Apache Airflow, PyCaret: Machine learning automation, pipeline scheduling.

• SpaCy, Hugging Face Transformers: Natural language processing and text analysis.