Data Scientist (2-5 Years Experience)

Key Responsibilities:

• Develop and implement machine learning and deep learning models for various business problems, with a strong focus on time series forecasting.
• Analyze large, complex datasets to extract actionable insights and identify trends, patterns, and opportunities for improvement.
• Design, build, and validate predictive models using state-of-the-art techniques, ensuring scalability and robustness.
• Collaborate with cross-functional teams (Product, Engineering, Business) to translate business requirements into data science solutions.
• Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
• Stay updated with the latest research and advancements in machine learning, deep learning, and time series analysis, and proactively apply new techniques as appropriate.
• Mentor junior team members and contribute to a culture of continuous learning and innovation.

Required Skills & Qualifications:

• 2-5 years of hands-on experience in data science, machine learning, and statistical modelling.
• Strong expertise in time series forecasting (ARIMA, XGBoost, RandomForest, TFT, NHITS, etc.) and familiarity with deep learning frameworks (TensorFlow, PyTorch).
• Excellent programming skills in Python (preferred), with proficiency in libraries such as NumPy, Pandas, scikit-learn, and visualization tools (Matplotlib, Seaborn, Plotly).
• Solid conceptual understanding of machine learning algorithms, deep learning architectures, and statistical methods.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Ability to learn quickly and adapt to new technologies, tools, and methodologies.
• Strong problem-solving skills and a keen attention to detail.
• Excellent communication and presentation skills.

Preferred Qualifications:

• Experience with cloud platforms and MLOps tools.
• Exposure to big data technologies (Spark, Hadoop) is a plus.
• Masters degree in Computer Science, Statistics, Mathematics, or a related field.