Job Description

We are seeking an experienced Mid-Level Data Scientist for the development of data-driven solutions aimed at identifying and capitalizing on critical business opportunities. You will design and implement predictive models, integrate internal and external data sources, and recommend machine learning strategies that enhance decision-making. This role requires hands-on expertise in model development, strategic collaboration with cross-functional teams, and mentoring junior data scientists. You will contribute to shaping the departments strategy and manage complex projects with a focus on delivering high-impact results. 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.