{"id":1238926,"url":"https://alion.io/job/wenger-watson-inc-data-scientist","title":"Data Scientist","company":{"id":3801547,"name":"Wenger & Watson Inc.","domain":"wengerwatson.com","url":"https://alion.io/company/wenger-and-watson-inc","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India","Bengaluru, India","Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":49000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"GCP","optional":true},{"name":"NLP","optional":true},{"name":"PyTorch","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-09-17T05:25:55Z","employer_posted_date":null,"last_verified_at":"2026-09-17T05:25:55Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"Role Overview :\n\nThe role involves working with large and diverse datasets, developing robust analytical and machine learning models, generating actionable insights, and collaborating closely with product, engineering, and business teams.\n\nKey Responsibilities :\n\nData Science & Machine Learning :\n\n- Develop, train, validate, and deploy machine learning models to solve complex business and product problems.\n\n- Apply supervised and unsupervised learning techniques to structured and unstructured datasets.\n\n- Build predictive models for classification, regression, forecasting, recommendation, segmentation, and anomaly detection use cases.\n\n- Select appropriate algorithms based on business objectives, data characteristics, and model performance requirements.\n\n- Continuously improve model accuracy, scalability, robustness, and interpretability.\n\nStatistical Analysis & Modelling :\n\n- Apply statistical techniques to identify patterns, relationships, trends, and anomalies within large datasets.\n\n- Perform hypothesis testing, statistical inference, correlation analysis, and experimentation.\n\n- Design and analyse A/B tests and controlled experiments where required.\n\n- Develop statistical and mathematical models to support business and product decisions.\n\n- Evaluate model assumptions and ensure statistical validity of analytical outcomes.\n\nData Preparation & Feature Engineering :\n\n- Work with data engineering teams to identify and prepare relevant data sources.\n\n- Perform data cleaning, preprocessing, transformation, and exploratory data analysis.\n\n- Develop meaningful features from raw data to improve model performance.\n\n- Handle missing data, outliers, data inconsistencies, and other data-quality challenges.\n\n- Build reusable data preparation and feature engineering pipelines.\n\nPredictive Analytics :\n\n- Develop predictive models to forecast business outcomes and identify future trends.\n\n- Analyse customer, product, operational, and behavioural data to generate actionable insights.\n\n- Identify opportunities for optimization through predictive and prescriptive analytics.\n\n- Translate analytical findings into recommendations that can influence business and product strategy.\n\nModel Evaluation & Optimization :\n\n- Establish appropriate metrics to evaluate machine learning model performance.\n\n- Perform model validation, cross-validation, error analysis, and performance benchmarking.\n\n- Tune model hyperparameters and optimize algorithms for improved performance.\n\n- Monitor model behaviour and identify model drift or degradation after deployment.\n\n- Ensure models balance accuracy, interpretability, scalability, and business requirements.\n\nProductionization & Deployment :\n\n- Collaborate with data engineers and software engineers to productionize machine learning models.\n\n- Develop production-ready data science solutions using Python and relevant ML frameworks.\n\n- Create scalable model pipelines and APIs for integrating ML models into applications.\n\n- Support model deployment, monitoring, testing, and lifecycle management.\n\n- Follow engineering best practices around version control, testing, documentation, and reproducibility.\n\nBusiness & Product Analytics :\n\n- Partner with Product, Engineering, Business, and Operations teams to understand business problems and translate them into analytical solutions.\n\n- Convert complex analytical findings into clear business recommendations.\n\n- Present insights and model outcomes to technical and non-technical stakeholders.\n\n- Identify new opportunities where data science and machine learning can create measurable business impact.\n\nResearch & Innovation :\n\n- Stay current with developments in machine learning, statistical modelling, and data science.\n\n- Evaluate new algorithms, frameworks, and modelling techniques.\n\n- Conduct proof-of-concepts and experiments to assess new approaches.\n\n- Contribute to the development of reusable data science methodologies and best practices.\n\nRequired Skills :\n\n- 5 - 10 years of professional experience in Data Science, Machine Learning, Predictive Analytics, or a related field.\n\n- Strong programming skills in Python.\n\n- Strong understanding of Machine Learning algorithms and statistical modelling.\n\n- Experience with supervised and unsupervised learning techniques.\n\n- Strong knowledge of feature engineering, model evaluation, validation, and optimization.\n\n- Hands-on experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and relevant ML frameworks.\n\n- Strong understanding of statistical concepts including hypothesis testing, probability, distributions, regression, and experimental design.\n\n- Experience working with large datasets and complex data problems.\n\n- Strong SQL skills for data extraction, transformation, and analysis.\n\n- Experience taking machine learning models from development to production.\n\n- Strong analytical and problem-solving capabilities.\n\n- Excellent communication and stakeholder management skills.\n\nGood to Have :\n\n- Experience with Deep Learning and frameworks such as PyTorch or TensorFlow.\n\n- Experience with cloud platforms such as AWS, GCP, or Azure.\n\n- Exposure to distributed data processing technologies such as Spark.\n\n- Experience with recommendation systems, forecasting, NLP, fraud detection, or anomaly detection.\n\n- Knowledge of MLOps, model monitoring, CI/CD, and ML model lifecycle management.\n\n- Experience working in a product-based technology environment.\n\n- Experience solving large-scale business and customer analytics problems.\n\nQualifications :\n\n- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative discipline.\n\n- Strong hands-on experience applying data science to real-world business problems.\nSkills\nPython, Machine Learning, SQL, Predictive Analytics, Data Science, Pandas, Scikit-Learn, PyTorch, Tensorflow, Data Scientist","description_format":"text","description_chars":5906,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-25T16:00:00Z"}],"liveness":{"score":68,"band":"ok","label":"Likely open","p_open":1,"p_active":0.759,"p_room":0.9,"age_days":10,"expected_fill_days":24,"reasons":["seen:10","velocity","win:mid"],"computed_at":"2026-09-27T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/wenger-watson-inc-data-scientist","json_url":"https://alion.io/job/wenger-watson-inc-data-scientist.json","meta":{"generated_at":"2026-09-28T03:02:08Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1875,"day_limit":5000,"remaining_today":3125,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}